Artificial neural networks for diagnosis of hepatitis disease

Author

Ozyilmaz, Lale ; Yildirim, Tulay

Author_Institution

Dept. of Electr. & Commun. Eng., Yildiz Tech. Univ., Istanbul, Turkey

Volume

1

fYear

2003

fDate

20-24 July 2003

Firstpage

586

Abstract

Recently, neural networks have become a very important method in the field of medical diagnostics. The objective of this work is to diagnose hepatitis disease by using different neural network architectures. Standard feedforward networks and a hybrid network were investigated. Results obtained show that especially the hybrid network can be successfully used for diagnosing of hepatitis.

Keywords

backpropagation; diseases; liver; medical diagnostic computing; multilayer perceptrons; neural net architecture; patient diagnosis; radial basis function networks; sampling methods; OLS algorithm; adaptive learning; artificial neural networks; conic section function neural network; hepatitis disease diagnosis; hybrid network; medical diagnostics; multilayer perceptron structure; neural network architectures; ordinary least squares algorithm; radial basis function network structure; standard backpropagation; standard feedforward networks; Artificial neural networks; Backpropagation algorithms; Databases; Electronic mail; Feedforward neural networks; Liver diseases; Medical diagnosis; Medical diagnostic imaging; Neural networks; Proteins;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223422

Filename

1223422