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

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.