Associative Classification Approach for Diagnosing Cardiovascular Disease

  • Kiyong Noh
  • Heon Gyu Lee
  • Ho-Sun Shon
  • Bum Ju Lee
  • Keun Ho Ryu
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 345)


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.


Heart Rate Variability Association Rule Heart Rate Variability Analysis Cohesion Measure Associative Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Cohen.: Biomedical Signal Processing. CRC press, Boca Raton, FL (1988)Google Scholar
  2. 2.
    Conumel, P., ECG: Past and Future. Annals NY Academy of Sciences, Vol.601 (1990)Google Scholar
  3. 3.
    J. Pan: A Real-time QRS Detection Algorithm. IEEE Trans. Eng. 32 (1985) 230–236Google Scholar
  4. 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
  5. 5.
    Meste, H., Rix, P., Caminal.: Ventricular Late Potentials Characterization in Time-frequency Domain by Means of a Wavelet Transform. IEEE Trans. Biomed. Eng. 41 (1994) 625–634CrossRefGoogle Scholar
  6. 6.
    Thakor, N. V., Yi-Sheng, Z.: Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection. IEEE Trans. Biomed. Eng. 38 (1991) 785–794CrossRefGoogle Scholar
  7. 7.
    Kuo, D., Chen, G. Y.: Comparison of Three Recumbent Position on Vagal and Sympathetic Modulation using Spectral Heart Rate Variability in Patients with Coronary Artery Disease. American Journal of Cardiology, 81 (1998) 392–396CrossRefGoogle Scholar
  8. 8.
    Guzzetti, S., Magatelli, R., Borroni, E.: Heart Rate Variability in Chronic Heart Failure. American Neuroscience; Basic and Clmical, 90 (2001) 102–105Google Scholar
  9. 9.
    Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley, New York, (1973)zbMATHGoogle Scholar
  10. 10.
    Quinlan, J., C4.5: Programs for Machine Learning, Morgan Kaufmann. San Mateo, (1993)Google Scholar
  11. 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. 12.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In SIGMOD’00, Dallas, TX, (2000)Google Scholar
  13. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kiyong Noh
    • 1
  • Heon Gyu Lee
    • 2
  • Ho-Sun Shon
    • 2
  • Bum Ju Lee
    • 2
  • Keun Ho Ryu
    • 2
  1. 1.Korea Research Institute of Standards and ScienceKorea
  2. 2.Database LaboratoryChungbuk National UniversityCheongjuKorea

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