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

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dc.contributor.author Bazan-Socha, Stanisława
dc.contributor.author Bazan, Jan. G.
dc.contributor.author Buregwa-Czuma, Sylwia
dc.contributor.author Pardel, Przemysław W.
dc.contributor.author Sokolowska, Barbara
dc.date.accessioned 2014-11-14T17:59:22Z
dc.date.available 2014-11-14T17:59:22Z
dc.date.issued 2012
dc.identifier.citation Praca opublikowana jako: Bazan, J., G., Bazan-Socha, S., Buregwa-Czuma, S., Pardel, P., Sokolowska, B.: Predicting the presence of serious coronary artery disease based on 24 hour Holter ECG monitoring. In: M. Ganzha, L. Maciaszek, M. Paprzycki (Eds.), Proceedings of the Federated Conference on Computer Science and Information Systems (FedCSIS'2012), Wroclaw, Poland, September 9-12, 2012, pp. 279-286, IEEE Xplore - digital library (w INTERNECIE). Oryginalna publikacja jest dostępna na stronie http://ieeexplore.ieee.org/ (The original publication is available at http://ieeexplore.ieee.org/).
dc.identifier.uri http://repozytorium.ur.edu.pl/handle/item/670
dc.description pl_PL.UTF-8
dc.description.abstract 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. pl_PL.UTF-8
dc.description.sponsorship This work was supported by the grant N N516 077837 from the Ministry of Science and Higher Education of the Republic of Poland, the Polish National Science Centre (NCN) grant 2011/01/B/ST6/03867 and by the Polish National Centre for Research and Development (NCBiR) grant No. SP/I/1/77065/10 in frame of the the strategic scientific research and experimental development program: ``Interdisciplinary System for Interactive Scientific and Scientific-Technical Information''. pl_PL.UTF-8
dc.language.iso eng pl_PL.UTF-8
dc.publisher IEEE Xplore pl_PL.UTF-8
dc.title Predicting the presence of serious coronary artery disease based on 24 hour Holter ECG monitoring. pl_PL.UTF-8
dc.type conferenceObject pl_PL.UTF-8


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