The Diagnosis of Hepatitis Diseases by Support Vector Machines and Artificial Neural Networks

In this paper, we use support vector machine (SVM) and artificial neural networks to diagnosis hepatitis diseases. Furthermore, we use those networks to identify the type and the phase of disease. Considering the most important hepatitis cases leads us to six classes: hepatitis B (two phases), hepatitis C (two phases), non-viral hepatitis and no-hepatitis. For this purpose, we design various networks including RBF, GRNN, PNN, LVQ and SVM. The performance of each of them has studied and the best method is selected for each of classification tasks. The overall accuracy of diagnosis system is near 97%.