A comparative study on computerised diagnostic performance of hepatitis disease using ANNs

Authors:
Revna Acar Vural;Lale Özyilmaz;Tülay Yildirim
Affiliations:
Department of Electronics & Communications Eng., Yildiz Technical University, İstanbul, Turkey;Department of Electronics & Communications Eng., Yildiz Technical University, İstanbul, Turkey;Department of Electronics & Communications Eng., Yildiz Technical University, İstanbul, Turkey
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

Citing 4
Cited 0

Probabilistic neural networks

Neural Networks
Artificial Neural Networks

Artificial Neural Networks
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
A multiple classifier system for early melanoma diagnosis

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Artificial Neural Networks (ANNs) have been studied intensively in the field of computer science in recent years and have been shown to be a powerful tool for a variety of data-classification and pattern-recognition tasks. In this work, computerised diagnostic performance of hepatitis disease was investigated by various ANNs. Multilayer Perceptron, Radial Basis Function Neural Network, Conic Section Function Neural Network, Probabilistic Neural Network, and General Regression Neural Network structures have been used for this purpose. To determine diagnostic performance of networks for hepatitis disease, cross validation method and ROC analysis were applied.