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

Author

Rouhani, Modjtaba ; Haghighi, Mehdi Motavalli

Author_Institution

Islamic Azad Univ., Gonabad, Iran

fYear

2009

fDate

17-20 April 2009

Firstpage

456

Lastpage

458

Abstract

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%.

Keywords

medical diagnostic computing; patient diagnosis; radial basis function networks; regression analysis; support vector machines; GRNN; LVQ; PNN; RBF; SVM; artificial neural network; classification task; disease diagnosis system; disease phase; disease type; generalized regression neural network; hepatitis B; hepatitis C; learning vector quantization network; nonviral hepatitis; probabilistic neural network; radial basis function; support vector machine; Artificial neural networks; Computer science; Liver diseases; Neural networks; Neurons; Springs; Statistical analysis; Support vector machine classification; Support vector machines; Viruses (medical); GRNN; LVQ; PNN; RBF; SVM; hepatitis disease;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Technology - Spring Conference, 2009. IACSITSC '09. International Association of

Conference_Location

Singapore

Print_ISBN

978-0-7695-3653-8

Type

conf

DOI

10.1109/IACSIT-SC.2009.25

Filename

5169393