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International Symposium on Methodologies for Intelligent Systems

ISMIS 1999: Foundations of Intelligent Systems pp 349–357Cite as

Knowledge discovery in clinical databases: An experiment with rule induction and statistics

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1609))

Abstract

The main difference between empirical learning methods and KDD methods is that the latter approaches support discovery of knowledge in databases whereas the former ones focus on extraction of accurate knowledge from databases. Therefore, for application of KDD methods, domain experts’ interpretation of induced results is crucial. However, conventional approaches do not focus on this issue clearly. In this paper, several KDD methods are compared by using a common database and the induced results are interpreted by a medical expert, which enables us to characterize KDD methods more concretely and to show the importance of interaction between KDD researchers and domain experts.

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Authors and Affiliations

  1. Department of Information Medicine, Medical Research Institute, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, 113, Tokyo, Japan

    Shusaku Tsumoto

Authors
  1. Shusaku Tsumoto
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Zbigniew W. Raś Andrzej Skowron

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© 1999 Springer-Verlag Berlin Heidelberg

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Tsumoto, S. (1999). Knowledge discovery in clinical databases: An experiment with rule induction and statistics. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095121

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  • DOI: https://doi.org/10.1007/BFb0095121

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65965-5

  • Online ISBN: 978-3-540-48828-6

  • eBook Packages: Springer Book Archive

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