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)

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.

Keywords

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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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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