Designing and evaluation of a decision support system for prediction of coronary artery disease

AUTHORS

Asieh Khosravanian 1 , * , Seyyed Saeed Ayat 2

AUTHORS INFORMATION

1 MSc of Department of Computer and Engineering and IT, Payame Noor University, Iran.

2 Department of Computer and Engineering and IT, Payame Noor University, Iran.

ARTICLE INFORMATION

Hormozgan Medical Journal: 19 (6); e87642
Published Online: October 20, 2014
Article Type: Research Article
Received: February 13, 2014
Accepted: October 20, 2014

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Abstract

Introduction: Since human health is the issue of Medical Research, correct prediction of
results is of a high importance. This study applies probabilistic neural network (PNN) for
predicting coronary artery disease (CAD), because the PNN is stronger than other
methods.
Methods: In this descriptive-analytic study, The PNN method was implemented on 150
patients admitted to the Mazandaran Heart Center, sari. For designing the network, 80%
of the data were used for stage of network training, and the remained 20% were used for
stage of network testing. In order to implement the network, facilities and functions
existing in MATLAB 7.12.0 were used and simulation was conducted in a PC with
configurations of corei5 CPU, 2GHz processor, 4GB ram, under operating system of
Windows 7.
Results: After 5 times simulation and comparison of the models produced, sensitivity and
specificity rates obtained were 1 and 1. In the end, model correctly categorized some
healthy subjects who did not need angiography and the treatment related to coronary
artery disease.
Conclusion: Due to the high specificity index, this model prevents side effects of
angiography in patients who don't need such treatments. Moreover, due to high
sensitivity, it can diagnose the patients who really need such diagnostic measures.

Keywords

Coronary Artery Disease Prediction Probabilistic Neural Network

© 2016, Hormozgan Medical Journal. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
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