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Intelligent Diagnosis of Heart Diseases using Neural Network Approach

by RANJANA RAUT, S. V. DUDUL
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 2
Year of Publication: 2010
Authors: RANJANA RAUT, S. V. DUDUL
10.5120/31-140

RANJANA RAUT, S. V. DUDUL . Intelligent Diagnosis of Heart Diseases using Neural Network Approach. International Journal of Computer Applications. 1, 2 ( February 2010), 97-102. DOI=10.5120/31-140

@article{ 10.5120/31-140,
author = { RANJANA RAUT, S. V. DUDUL },
title = { Intelligent Diagnosis of Heart Diseases using Neural Network Approach },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 2 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 97-102 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number2/31-140/ },
doi = { 10.5120/31-140 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:43:52.460739+05:30
%A RANJANA RAUT
%A S. V. DUDUL
%T Intelligent Diagnosis of Heart Diseases using Neural Network Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 2
%P 97-102
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Experiments with the Switzerland Heart Disease database have concentrated on attempting to distinguish presence and absence. The classifiers based on various neural networks, namely, MLP, PCA, Jordan, GFF, Modular, RBF, SOFM, SVM NNs and conventional statistical techniques such as DA and CART are optimally designed, thoroughly examined and performance measures are compared in this study. With chosen optimal parameters of MLP NN, when it is trained and tested over cross validation (unseen data sets), the average (and best respectively) classification of 98±2.83 % (and 100%), 96.67±4.56% overall accuracy, sensitivity 96±5.48, specificity 100% are achieved which shows consistent performance than other NN and statistical models. The results obtained in this work show the potentiality of the MLP NN approach for heart diseases classification.

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Index Terms

Computer Science
Information Sciences

Keywords

Heart disease MLP neural network Error back propagation algorithm Performance