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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)

Abstract

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.

Keywords

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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References

  1. 1.
    Adams RD and Victor M: Principles of Neurology, 5th edition. McGraw-Hill, New York, 1993.Google Scholar
  2. 2.
    Buchnan BG and Shortliffe EH(Eds): Rule-Based Expert Systems. Addison-Wesley, 1984.Google Scholar
  3. 3.
    Matsumura Y, Matsunaga T, Hata Y, Kimura M, Matsumura H: Consultation system for diagnoses of headache and facial pain: RHINOS. Medical Informatics 11: 145–157, 1988.Google Scholar
  4. 4.
    Matsumura Y, Matsunaga T, Maeda Y, Tsumoto S, Matsumura H, Kimura M: Consultation System for Diagnosis of Headache and Facial Pain: “RHINOS”. Proceedings of Logic Prgram Conferences, pp. 287–298, 1985.Google Scholar
  5. 5.
    Michalski RS, Mozetic I, Hong J, and Lavrac N: The Multi-Purpose Incremental Learning System AQ15 and its Testing Application to Three Medical Domains. Proceedings of the fifth National Conference on Artificial Intelligence, AAAI Press, Palo Alto CA, pp 1041–1045, 1986.Google Scholar
  6. 6.
    Pawlak Z: Rough Sets. Kluwer Academic Publishers, Dordrecht, 1991.MATHCrossRefGoogle Scholar
  7. 7.
    Pawlak Z: Rough Modus Ponens. In: Proceedings of International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems 98, Paris, 1998.Google Scholar
  8. 8.
    Quinlan JR: C4.5 - Programs for Machine Learning. Morgan Kaufmann, Palo Alto CA, 1993.Google Scholar
  9. 9.
    Rissanen J: Stochastic Complexity in Statistical Inquiry. World Scientific, Singapore, 1989.MATHGoogle Scholar
  10. 10.
    Skowron, A. and Grzymala-Busse, J. From rough set theory to evidence theory. In: Yager, R., Fedrizzi, M. and Kacprzyk, J.(eds.) Advances in the Dempster-Shafer Theory of Evidence, pp. 193–236, John Wiley & Sons, New York, 1994.Google Scholar
  11. 11.
    Shavlik JW and Dietterich TG(Eds): Readings in Machine Learning. Morgan Kaufmann, Palo Alto CA, 1990.Google Scholar
  12. 12.
    Tsumoto S and Tanaka H: Automated Discovery of Medical Expert System Rules from Clinical Databases based on Rough Sets. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining 96, AAAI Press, Palo Alto CA, pp. 63–69, 1996.Google Scholar
  13. 13.
    Tsumoto S: Modelling Medical Diagnostic Rules based on Rough Sets. In: Polkowski L and Skowron A (Eds): Rough Sets and Current Trends in Computing, Lecture Note in Artificial Intelligence 1424, 1998.Google Scholar
  14. 14.
    Tsumoto S: Automated Extraction of Medical Expert System Rules from Clinical Databases based on Rough Set Theory. Information Sciences, 112, 67–84, 1998.CrossRefGoogle Scholar
  15. 15.
    Ziarko W: Variable Precision Rough Set Model. Journal of Computer and System Sciences 46: 39–59, 1993.MathSciNetMATHCrossRefGoogle Scholar

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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