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Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling
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Author(s): Markos G. Tsipouras (University of Ioannina, Greece), Themis P. Exarchos (University of Ioannina, Greece), Dimitrios I. Fotiadis (University of Ioannina, Greece, Michaelideion Cardiology Center, Greece, and Biomedical Research Institute, Greece), Aris Bechlioulis (University of Ioannina, Greece, and Michaelideion Cardiology Center, Greece)and Katerina K. Naka (University of Ioannina, Greece, and Michaelideion Cardiology Center, Greece)
Copyright: 2008
Pages: 9
Source title:
Encyclopedia of Healthcare Information Systems
Source Author(s)/Editor(s): Nilmini Wickramasinghe (Illinois Institute of Technology, USA)and Eliezer Geisler (Illinois Institute of Technology, USA)
DOI: 10.4018/978-1-59904-889-5.ch051
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Abstract
This article addresses the decision support regarding cardiovascular diseases, using computer-based methods, focusing on the coronary artery disease (CAD) diagnosis and on the prediction of clinical restenosis in patients undergoing angioplasty. Methods reported in the literature are reviewed with respect to (i) the medical information that are employing in order to reach the diagnosis and (ii) the data analysis techniques used for the creation of the CDSSs. In what concerns medical information, easily and noninvasively-obtained data present several advantages compared to other types of data, while data analysis techniques that are characterized by transparency regarding their decisions are more suitable for medical decision making. A recently developed approach that complies with the above requirements is presented. The approach is based on data mining and fuzzy modelling. Using this approach, one CDSS has been developed for each of the two cardiovascular problems mentioned above. These CDSSs are extensively evaluated and comments about the discovered knowledge are provided by medical experts. The later is of great importance in designing and evaluating CDSSs, since it allows them to be integrated into real clinical environments.
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