Automated Discovery of Medical Expert System Rules from Clinical Databases Based on Rough Sets

Shusaku Tsumoto, Hiroshi Tanaka

Automated knowledge acquisition is an important research issue to solve the bottleneck problem in developing expert systems. Although many inductive learning methods have been proposed for this purpose, most of the approaches focus only on inducing classification rules. However, medical experts also learn other information important for diagnosis from clinical cases. In this paper, a rule induction method is introduced, which extracts not only classification rules but also other medical knowledge needed for diagnosis. This system is evaluated on a clinical database of headache, whose experimental results show that our proposed method correctly induces diagnostic rules and estimates the statistical measures of rules.

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