Short Communication: Assessment of exercise stress testing with artificial neural network in determining coronary artery disease and predicting lesion localization

Authors:
Ismail Babaoglu;Omer Kaan Baykan;Nazif Aygul;Kurtulus Ozdemir;Mehmet Bayrak
Affiliations:
Department of Computer Engineering, Selcuk University, Konya, Turkey;Department of Computer Engineering, Selcuk University, Konya, Turkey;Department of Cardiology, Medicine Faculty of Meram, Selcuk University, Konya, Turkey;Department of Cardiology, Medicine Faculty of Meram, Selcuk University, Konya, Turkey;Department of Electric-Electronics Engineering, Selcuk University, Konya, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 6
Cited 3

Artificial Immune Systems: A New Computational Intelligence Paradigm

Artificial Immune Systems: A New Computational Intelligence Paradigm
Classification of Transcranial Doppler Signals Using Artificial Neural Network

Journal of Medical Systems
A decision support system based on support vector machines for diagnosis of the heart valve diseases

Computers in Biology and Medicine
Classification of carotid artery Doppler signals in the early phase of atherosclerosis using complex-valued artificial neural network

Computers in Biology and Medicine
Predicting breast cancer survivability: a comparison of three data mining methods

Artificial Intelligence in Medicine
A fuzzy clustering neural network architecture for classification of ECG arrhythmias

Computers in Biology and Medicine

Effects of discretization on determination of coronary artery disease using support vector machine

Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Effects of principle component analysis on assessment of coronary artery diseases using support vector machine

Expert Systems with Applications: An International Journal
Design of a Fuzzy-based Decision Support System for Coronary Heart Disease Diagnosis

Journal of Medical Systems

Quantified Score

Hi-index	12.05

Visualization

Abstract

The aim of this study is to show the artificial neural network (ANN) on determination of coronary artery disease existence and localization of lesion based upon exercise stress testing (EST) data. EST and coronary angiography were performed on 330 patients. The data studied acquiring 27 verifying features was normalized employing z-score method. To select training and test data, 10-fold cross-validation methods were involved and multi-layered perceptron neural network was employed for the classification. The interpretation of EST using ANN proved 91%, 73% and 65% diagnostic accuracy for the left main coronary (LMCA), left anterior descending and left circumflex coronary arteries, respectively. Besides, 69% for the right coronary artery is also predicted. For the LMCA, a 94% negative predictive value (NPV) was obtained. This high percentage of NPV encourages the elimination of LMCA lesions. Some knowledge can also be obtained about lesion localization, besides diagnosing of coronary artery disease by the assessment of EST via ANN.