Abstract
The aim of the study was to assess the usefulness of classification methods in recognizing cardiovascular pathology. From the medical point of view the study involves prediction of coronary arteriosclerosis presence in patient with stable angina using clinical data and electrocardiogram (ECG) Holter monitoring records. On the grounds of these findings the need for coronary interventions is determined. 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. The paper includes results of experiments that have been performed on medical data obtained from II Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland.
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Bazan, J.G., Bazan-Socha, S., Buregwa-Czuma, S., Pardel, P.W., Sokolowska, B. (2012). Prediction of Coronary Arteriosclerosis in Stable Coronary Heart Disease. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances in Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 298. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31715-6_58
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DOI: https://doi.org/10.1007/978-3-642-31715-6_58
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