Human Readable Rule Induction in Medical Data Mining

  • Nor Ridzuan Daud
  • David Wolfe Corne
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 27)


In general, the human-readable rule refers to data shown in a format easily read by most humans—normally this is in the form of IF…THEN rules. This is the most convenient way for physicians to express their knowledge in medical diagnosis. In particular, if learned diagnostic rules can be presented in such a form, physicians are much more likely to trust and believe the consequent diagnoses. This paper investigates the performances of existing state-of-the-art classification algorithms, mainly rule induction and tree algorithms, on benchmark problems in medical data mining. The findings indicate that certain algorithms are better for generating rules that are both accurate and short; these algorithms are recommended for further research towards the goal of improved accuracy and readability in medical data mining.


Rule Induction Decision Tree Learner Rule Algorithm Rule Induction Algorithm Small Error Rate 
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 Science+Business Media, LLC 2009

Authors and Affiliations

  • Nor Ridzuan Daud
    • 1
  • David Wolfe Corne
    • 2
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaMalaysia
  2. 2.School of Mathematical and Computer SciencesHeriot-Watt UniversityEdinburghUK

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