Detection of CAN by Ensemble Classifiers Based on Ripple Down Rules

  • Andrei Kelarev
  • Richard Dazeley
  • Andrew Stranieri
  • John Yearwood
  • Herbert Jelinek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7457)


It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.


Consensus Function Ensemble Method Heart Rate Change Cardiovascular Autonomic Neuropathy Cardiac Autonomic Neuropathy 
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 2012

Authors and Affiliations

  • Andrei Kelarev
    • 1
  • Richard Dazeley
    • 1
  • Andrew Stranieri
    • 1
  • John Yearwood
    • 1
  • Herbert Jelinek
    • 2
  1. 1.Centre for Informatics and Applied Optimization, School of SITEUniversity of BallaratBallaratAustralia
  2. 2.Centre for Research in Complex Systems and School of Community HealthCharles Sturt UniversityAlburyAustralia

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