Induction of medical knowledge based on rough sets and cross-validation method

S. Tsumoto, Hiroshi Tanaka

研究成果: Article査読

抄録

Several symbolic methods of inductive learning have been proposed such as decision trees and the AQ family. These methods are applied to extract meaningful knowledge from a large database, and their application is in some aspects appropriate. However, in most cases, the methods are of deterministic nature, and the reliability of acquired knowledge is not strictly evaluated from a statistical point of view. These methods are therefore not suitable to apply to a domain of essentially probabilistic nature like that of medicine. By extending the concepts of Pawlak's rough set theory to the probabilistic domain, we introduce a new approach to knowledge acquisition called PRIMEROSE that induces probabilistic rules. We also describe a program that extracts rules from a clinical database for use with an expert system by using this method. The program is applied to three medical domains: headache, meningoencephalitis, and cerebrovascular diseases. The results show that the derived rules almost correspond to those of medical experts.

本文言語English
ページ(範囲)91-99
ページ数9
ジャーナルJapan Journal of Medical Informatics
16
2
出版ステータスPublished - 1996 1 1

ASJC Scopus subject areas

  • 健康情報学

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