Effective diagnosis of coronary artery disease using the rotation forest ensemble method

Coronary Artery Disease is a common heart disease related to disorders effecting the heart and blood vessels. Since the disease is one of the leading causes of heart attacks and thus deaths, diagnosis of the disease in its early stages or in cases when patients do not show many of the symptoms yet has considerable importance. In the literature, studies based on computational methods have been proposed to diagnose the disease with readily available and easily collected patient data, and among these studies, the greatest accuracy reached is 89.01%. This paper presents a computational tool based on the Rotation Forest algorithm to effectively diagnose Coronary Artery Disease in order to support clinical decision-making processes. The proposed method utilizes Artificial Neural Networks with the Levenberg-Marquardt back propagation algorithm as base classifiers of the Rotation Forest ensemble method. In this scheme, 91.2% accuracy in diagnosing the disease is accomplished, which is, to the best of our knowledge, the best performance among the computational methods from the literature that use the same data. This paper also presents a comparison of the proposed method with some other classifiers in terms of diagnosis performance of Coronary Artery Disease.