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  Implementation of Neuro Fuzzy System for Diagnosis of Multiple Sclerosis  
  Authors : Mohammad Esmaeil Shaabani; Touraj Banirostam; Alireza Hedayati
  Cite as:

 

Medical diagnosis is often done by expertise and experience of phisician, but sometimes may lead to misdiagnosis. Multiple sclerosis (MS) is a disease of the central nervous system. In this disease, body produces antibodies that attack and damage the Myelin, in which the myelin sheath (the insulation for nerve fibers) is in trouble and the damage to myelin in the central nervous system cause to disconnect between brain and other organs. The major problem is the lack of diagnosis. To improve diagnosis, Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. ANFIS main idea is that using the way the nervous system of biological for data processing in order to learn and create the knowledge. This system uses neural network for learning, classification capabilities and modifying. There are several ways to train neural network. In this study, we use hybrid approach to train. This hybrid approach uses Back Propagation(BP) and Least Square Error(LSE). ANFIS has the ability to combine the linguistic power of fuzzy system with numeric power of neural network. For optimizing the input/output, the K-fold cross validation has been used. Implementation has been done in MATLAB. The Data set consist of 600 patients that each one has 6 columns, 5 of them is input and 1 of them is output that shows diagnosis.

 

Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 157-164

Figures :12

Tables : --

Publication Link : Implementation of Neuro Fuzzy System for Diagnosis of Multiple Sclerosis

 

 

 

Mohammad Esmaeil Shaabani : Department of Computer Engineering, Tehran Center Branch, Islamic Azad University Tehran, Iran

Touraj Banirostam : Department of Computer Engineering, Tehran Center Branch, Islamic Azad University Tehran, Iran

Alireza Hedayati : Department of Computer Engineering, Tehran Center Branch, Islamic Azad University Tehran, Iran

 

 

 

 

 

 

 

Multiple Sclerosis

Fuzzy System

Neural Network

ANFIS

Hybrid Learning

Decision support system play an important role in patient care. False detection in each kind of diseases have an irreparable damage to patients and clinican. The aim of this study is improve diagnosis of MS. We had tried to bring the learning ability of neural network into fuzzy inference system to improve the diagnosis of MS. We use 600 patient's data consist of 5 features and use hybrid learning algorithm in ANFIS that applied Least Square Estimation (LSE) and Back Propagation (BP) to reduce the diagnosis error. For optimize the input data and evaluate the performance of our system we use K-fold cross validation. Proposed system compared with ANFIS with BP algorithm and GRNN. Simulation result show that proposed system has almost 96% accuracy.

 

 

 

 

 

 

 

 

 

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