Discovery of Positive and Negative Knowledge in Medical Databases Using Rough Sets
- Shusaku Tsumoto
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One of the most important problems on rule induction methods is that extracted rules partially 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, and 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.
- Adams RD and Victor M: Principles of Neurology, 5th edition. McGraw-Hill, New York, 1993.
- Buchnan BG and Shortliffe EH(Eds): Rule-Based Expert Systems. Addison-Wesley, 1984.
- 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.
- Pawlak Z: Rough Sets. Kluwer Academic Publishers, Dordrecht, 1991.
- Pawlak Z: Rough Modus Ponens. In: Proceedings of International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems 98, Paris, 1998.
- Quinlan JR: C4.5-Programs for Machine Learning. Morgan Kaufmann, Palo Alto CA, 1993.
- 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.
- Shavlik JW and Dietterich TG(Eds): Readings in Machine Learning. Morgan Kaufmann, Palo Alto CA, 1990.
- 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.
- 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.
- Tsumoto S: Automated Extraction of Medical Expert System Rules from Clinical Databases based on Rough Set Theory. Journal of Information Sciences, 1998
- Tsumoto S: Knowledge discovery in clinical databases and evaluation of discovered knowledge in outpatient clinic. Information Sciences, 124, 125–137, 2000. CrossRef
- Tsumoto S: Automated Discovery of Positive and Negative Knowledge in Clinical Databases. IEEE BME Magazine, 19, 56–62, 2000. CrossRef
- Ziarko W: Variable Precision Rough Set Model. Journal of Computer and System Sciences 46:39–59, 1993. CrossRef
- Discovery of Positive and Negative Knowledge in Medical Databases Using Rough Sets
- Book Title
- Progress in Discovery Science
- Book Subtitle
- Final Report of the Japanese Dicsovery Science Project
- pp 543-552
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 1. Department of Informatics, Kyushu University
- Shusaku Tsumoto (4)
- Author Affiliations
- 4. Department of Medicine Informatics, Shimane Medical University, School of Medicine, 89-1 Enya-cho Izumo City, 693-8501, Shimane, Japan
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