Abstract
ECG is a test that measures a heart’s electrical activity, which provides valuable clinical information about the heart’s status. In this paper, we propose a classification method for extracting multi-parametric features by analyzing HRV from ECG, data preprocessing and heart disease pattern. The proposed method is an associative classifier based on the efficient FP-growth method. Since the volume of patterns produced can be large, we offer a rule cohesion measure that allows a strong push of pruning patterns in the pattern-generating process. We conduct an experiment for the associative classifier, which utilizes multiple rules and pruning, and biased confidence (or cohesion measure) and dataset consisting of 670 participants distributed into two groups, namely normal people and patients with coronary artery disease.
This works was supported by the Regional Research Centers Program of Ministry of Education & Human Resources Development in Korea, and Korea Science and Engineering Foundation (#1999-2-303-006-3).
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Noh, K., Lee, H.G., Shon, HS., Lee, B.J., Ryu, K.H. (2006). Associative Classification Approach for Diagnosing Cardiovascular Disease. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_82
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DOI: https://doi.org/10.1007/978-3-540-37258-5_82
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