Diagnosing Hepatitis Disease by Using Fuzzy Hopfield Neural Network

Abstract

Aims: Nowadays, computational intelligence is frequently used in diagnosis and determination of the severity of various diseases. In fact, different tools of computational intelligence help physicians as an assistant to diagnose with fewer errors. In this paper, a fuzzy Hopfield neural network has been used as the determination of severity of the famous disease of hepatitis. Study Design: This disease is one of the most common and dangerous diseases which endanger the lives of millions of people every year. Diagnosing this disease has always been a serious challenge for physicians and thus we hope this study to be helpful. Place and Duration of Study: Department of Medicine, Mashhad University and hospital of imam reza, department of liver biopsy, Mashhad, Iran. Methodology: The data was extracted from University of California, Irvine (UCI) and it has 19 fields with 155 records. It was used the fuzzy Hopfield neural network and the comparison of its performance with various neural networks Multilayer Perceptron (MLP). This trained by standard back propagation, Radial Basis Function (RBF) network, the structure trained by Orthogonal Least Squares (OLS) algorithm, General Regression Neural Networks (GRNN), Bayesian Network with Naïve Dependence and Feature selection (BNNF), Bayesian Network with Naïve Dependence (BNND) and Hopfield Neural Network (HNN). Results: it was found that it has a good performance and was able to diagnose the severity of hepatitis with 92.05% accuracy. Conclusion: In this article, it is tried to diagnose hepatitis more accurately by fuzzy Hopfield neural network. This network has a high convergence speed and does not have the main problem of the Hopfield network which may converge on another model different from the input data. The use of a suitable pre-processing tool on the data has contributed greatly to the better training of the networks. The training data was not used in network testing in order to get more realistic consequences.