Advances in Information Technology

Volume 114 of the series Communications in Computer and Information Science pp 18-26

A Comparison of Artificial Intelligence Methods on Determining Coronary Artery Disease

  • İsmail BabaoğluAffiliated withDepartment of Computer Engineering, Selçuk University
  • , Ömer Kaan BaykanAffiliated withDepartment of Computer Engineering, Selçuk University
  • , Nazif AygülAffiliated withDepartment of Cardiology, Selçuklu Faculty of Medicine, Selçuk University
  • , Kurtuluş ÖzdemirAffiliated withDepartment of Cardiology, Meram Faculty of Medicine, Selçuk University
  • , Mehmet BayrakAffiliated withDepartment of Electrical and Electronics Engineering, Mevlana University

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The aim of this study is to show a comparison of multi-layered perceptron neural network (MLPNN) and support vector machine (SVM) on determination of coronary artery disease existence upon exercise stress testing (EST) data. EST and coronary angiography were performed on 480 patients with acquiring 23 verifying features from each. The robustness of the proposed methods is examined using classification accuracy, k-fold cross-validation method and Cohen’s kappa coefficient. The obtained classification accuracies are approximately 78% and 79% for MLPNN and SVM respectively. Both MLPNN and SVM methods are rather satisfactory than human-based method looking to Cohen’s kappa coefficients. Besides, SVM is slightly better than MLPNN when looking to the diagnostic accuracy, average of sensitivity and specificity, and also Cohen’s kappa coefficient.


Exercise stress testing coronary artery disease support vector machine artificial neural networks