Title :
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
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;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223422
