Predicting the presence of serious coronary artery disease based on 24 hour Holter ECG monitoring.

The purpose of this study was to evaluate the usefulness of classification methods in recognizing cardiovascular pathology. Based on clinical and electrocardiographic (ECG) Holter data we propose the method for predicting coronary stenosis demanding revascularization in patients with diagnosis of stable coronary heart disease. An approach to solving this problem has been found in the context of rough set theory and methods. Rough set theory introduced by Zdzisław Pawlak during the early 1980s provides the foundation for the construction of classifiers. From the rough set perspective, classifiers presented in the paper are based on a decision tree calculated on the basis of the local discretization method. We present a new modification of tree building method which emphasizes the discernibility of objects belonging to decision classes indicated by human experts. Presented method may be used to assess the need for revascularization and in special circumstances, to confirm or reject the diagnosis of coronary artery disease. The paper includes results of experiments that have been performed on medical data obtained from Second Department of Internal Medicine, Collegium Medicum, Jagiellonian University, Krakow, Poland.