Determination of The Neural Network Performances In The Medical Prognosis By Roc Analysis

Author

Tokan, Fikret ; Turker, N. ; Yildirim, Tülay

Author_Institution

Elektrik ve Elektron. Muhendisligi Bolumu, Yildiz Teknik Univ., Besiktas

fYear

2006

fDate

17-19 April 2006

Firstpage

1

Lastpage

4

Abstract

Recently, artificial neural networks are widely used in medical prognosis. The goal of this work is to predict whether a patient will live at least one year after a heart attack by using neural networks as an example of prognosis. With this aim, multi layer perceptrons (MLP), radial basis function networks (RBF), probabilistic neural networks (PNN), generalized regression neural networks (GRNN) and learning vector quantization networks (LVQ) are used. To demonstrate the real performances of the networks, not only classification accuracies but also receiver operation characteristics (ROC) analysis must be investigated. For this purpose, both sensitivity-specificity values and ROC curves are evaluated for all networks

Keywords

learning (artificial intelligence); medical computing; multilayer perceptrons; patient diagnosis; radial basis function networks; vector quantisation; GRNN; LVQ network; MLP; PNN; RBF; ROC analysis; Roc analysis; artificial neural network; generalized regression neural network; learning vector quantization; medical prognosis; multilayer perceptron; probabilistic neural network; radial basis function network; receiver operation characteristics; Artificial neural networks; Cardiac arrest; Influenza; Intelligent networks; Neural networks; Performance analysis; Performance evaluation; Radial basis function networks; Testing; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications, 2006 IEEE 14th

Conference_Location

Antalya

Print_ISBN

1-4244-0238-7

Type

conf

DOI

10.1109/SIU.2006.1659802

Filename

1659802