Associative Classification Approach for Diagnosing Cardiovascular Disease
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.
KeywordsHeart Rate Variability Association Rule Heart Rate Variability Analysis Cohesion Measure Associative Classifier
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- 1.Cohen.: Biomedical Signal Processing. CRC press, Boca Raton, FL (1988)Google Scholar
- 2.Conumel, P., ECG: Past and Future. Annals NY Academy of Sciences, Vol.601 (1990)Google Scholar
- 3.J. Pan: A Real-time QRS Detection Algorithm. IEEE Trans. Eng. 32 (1985) 230–236Google Scholar
- 4.Taddei, G., Constantino, Silipo, R.: A System for the Detection of Ischemic Episodes in Ambulatory ECG. Computers in Cardiology, IEEE Comput. Soc. Press, (1995) 705–708Google Scholar
- 8.Guzzetti, S., Magatelli, R., Borroni, E.: Heart Rate Variability in Chronic Heart Failure. American Neuroscience; Basic and Clmical, 90 (2001) 102–105Google Scholar
- 10.Quinlan, J., C4.5: Programs for Machine Learning, Morgan Kaufmann. San Mateo, (1993)Google Scholar
- 11.Liu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In Proc. of the 4th International Conference Knowledge Discovery and Data Mining, (1998)Google Scholar
- 12.Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In SIGMOD’00, Dallas, TX, (2000)Google Scholar
- 13.Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification Based on Multiple Association Rules. In Proc. of 2001 International Conference on Data Mining, (2001)Google Scholar
- 14.Jin Suk Kim, Hohn Gyu Lee, Sungbo Seo, Keun Ho Ryu: CTAR: Classification Based on Temporal Class-Association Rules for Intrusion Detection. In Proc, of the 4th International Workshop on Information Security Applications, (2003) 101–113Google Scholar