A Comparison of Artificial Intelligence Methods on Determining Coronary Artery Disease

  • İsmail Babaoğlu
  • Ömer Kaan Baykan
  • Nazif Aygül
  • Kurtuluş Özdemir
  • Mehmet Bayrak
Part of the Communications in Computer and Information Science book series (CCIS, volume 114)


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 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • İsmail Babaoğlu
    • 1
  • Ömer Kaan Baykan
    • 1
  • Nazif Aygül
    • 2
  • Kurtuluş Özdemir
    • 3
  • Mehmet Bayrak
    • 4
  1. 1.Department of Computer EngineeringSelçuk UniversityKonyaTurkey
  2. 2.Department of Cardiology, Selçuklu Faculty of MedicineSelçuk UniversityKonyaTurkey
  3. 3.Department of Cardiology, Meram Faculty of MedicineSelçuk UniversityKonyaTurkey
  4. 4.Department of Electrical and Electronics EngineeringMevlana UniversityKonyaTurkey

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