FUZZY BASED PREDICTION OF MODIFIED RANKINE SCALE -MRS- OF PATIENTS WITH INTRACRANIAL ANEURYSM
نویسنده:
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چکیده: Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making.
It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology.
Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still
major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many
factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing
the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach
can be used as an ef ficient predictor.
Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem
hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients
were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-,
rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher
scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy
system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and
subconscious knowledge of the expert into fuzzy IF-THEN rules.
Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for
verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs.
Conclusion: Accurate and early outcome prediction of the
کلیدواژه(گان): Subarachnoid Hemorrhage,Intracranial Aneurysm,Fuzzy Based Prediction,Modified Rankine Scale
کالکشن
:
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آمار بازدید
FUZZY BASED PREDICTION OF MODIFIED RANKINE SCALE -MRS- OF PATIENTS WITH INTRACRANIAL ANEURYSM
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| contributor author | یاسمن عقلی | en |
| contributor author | حمید معین فرد | en |
| contributor author | هومن بهاروحدت | en |
| contributor author | Yasaman Aghli | fa |
| contributor author | Hamid Moeenfard | fa |
| contributor author | Humain Baharvahdat | fa |
| date accessioned | 2020-06-06T13:44:29Z | |
| date available | 2020-06-06T13:44:29Z | |
| identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/3367197 | |
| description abstract | Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the patient is necessary for any medical decision making. It is investigated that the outcome of the patient with IA could be predicted efficiently by fuzzy logic methodology. Thus, this research can pioneer new studies in neurosur gery area.Background & Aim: Subarachnoid hemorrhage -SAH- resulting from ruptured intracranial aneurysm -IA- is a still major cause of death and disability. Early prediction of outcome after SAH lacks accuracy since there are many factors and uncertainties in the patient’s clinical status. It is essential to determine the severity of SAH for managing the surgical procedures. Statistical techniques cannot processed these uncertainties simply. Fuzzy logic approach can be used as an ef ficient predictor. Methods & Materials/Patients: This study was conducted retrospectively in 423 patients who admitted to Ghaem hospital of Mashhad with the diagnosis of SAH due to IA between December 2012 and April 2016. The patients were assessed by ten significant variables; including World Federation of Neurological Surgeons scale -WFNS-, rebleeding before operation, age, severespasm, External Ventricular Drainage -EVD-, ischemia, modified Fisher scale -mFisher-, infection, hydrocephalus and the operation method whether it was clipping or coiling. The fuzzy system predicts modified Rankine scale -mRs- based on table look up scheme which converts conscious and subconscious knowledge of the expert into fuzzy IF-THEN rules. Results: In this study, 300 patients is used for constructing the fuzzy rule base and 123 patients were assessed for verification of the fuzzy system. Fuzzy logic predictions correl ate with the patients’ real mRs. Conclusion: Accurate and early outcome prediction of the | en |
| title | FUZZY BASED PREDICTION OF MODIFIED RANKINE SCALE -MRS- OF PATIENTS WITH INTRACRANIAL ANEURYSM | en |
| type | Journal Paper | |
| contenttype | External Fulltext | |
| subject keywords | Subarachnoid Hemorrhage | en |
| subject keywords | Intracranial Aneurysm | en |
| subject keywords | Fuzzy Based Prediction | en |
| subject keywords | Modified Rankine Scale | en |
| identifier articleid | 1073334 |