Discovery of Approximate Knowledge in Medical Databases Based on Rough Set Model

  • Shusaku Tsumoto
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 95)


One of the most important problems on rule induction methods is that extracted rules do not plausibly represent information on experts’ decision processes, which makes rule interpretation by domain experts difficult. In order to solve this problem, the characteristics of medical reasoning is discussed positive and negative rules are introduced which model medical experts’ rules. Then, for induction of positive and negative rules, two search algorithms are provided. The proposed rule induction method was evaluated on medical databases, the experimental results of which show that induced rules correctly represented experts’ knowledge and several interesting patterns were discovered.


Classification Accuracy Bacterial Meningitis True Positive Rate Medical Expert Target Concept 
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 2002

Authors and Affiliations

  • Shusaku Tsumoto
    • 1
  1. 1.Department of Medical InformaticsShimane Medical University, School of MedicineIzumoJapan

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