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Predicting the Presence of Serious Coronary Artery Disease Based on 24 Hour Holter ECG Monitoring

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Book cover Transactions on Rough Sets XIX

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 8988))

Abstract

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

This work was partially supported by two grants of the Polish National Science Centre: DEC-2012/05/B/ST6/03215 and DEC-2013/09/B/ST6/01568, and also by the Centre for Innovation and Transfer of Natural Sciences and Engineering Knowledge of University of Rzeszów, Poland.

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Authors and Affiliations

  1. Institute of Computer Science, University of Rzeszów, Pigonia 1, 35-959, Rzeszów, Poland

    Jan G. Bazan, Sylwia Buregwa-Czuma & Przemysław Wiktor Pardel

  2. II Department of Internal Medicine, Jagiellonian University Medical College, Skawińska 8 Street, 31-066, Kraków, Poland

    Stanisława Bazan-Socha, Barbara Sokołowska & Sylwia Dziedzina

Authors
  1. Jan G. Bazan
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  2. Sylwia Buregwa-Czuma
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  3. Przemysław Wiktor Pardel
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  4. Stanisława Bazan-Socha
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  5. Barbara Sokołowska
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  6. Sylwia Dziedzina
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Corresponding author

Correspondence to Sylwia Buregwa-Czuma .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, Canada

    James F. Peters

  2. University of Warsaw, Warsaw, Poland

    Andrzej Skowron

  3. University of Warsaw, Warsaw, Poland

    Dominik Ślȩzak

  4. University of Warsaw, Warsaw, Poland

    Hung Son Nguyen

  5. University of Rzeszów, Rzeszów, Poland

    Jan G. Bazan

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Bazan, J.G., Buregwa-Czuma, S., Pardel, P.W., Bazan-Socha, S., Sokołowska, B., Dziedzina, S. (2015). Predicting the Presence of Serious Coronary Artery Disease Based on 24 Hour Holter ECG Monitoring. In: Peters, J., Skowron, A., Ślȩzak, D., Nguyen, H., Bazan, J. (eds) Transactions on Rough Sets XIX. Lecture Notes in Computer Science(), vol 8988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47815-8_7

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  • DOI: https://doi.org/10.1007/978-3-662-47815-8_7

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