Automated knowledge acquisition from clinical databases based on rough sets and attribute-oriented generalization

Rule induction methods have been proposed in order to acquire knowledge automatically from databases However conventional approaches do not focus on the implementation of induced results into an expert system In this paper, the author focuses not only, on rule induction bid also on its evaluation and presents a systematic approach from the former to the latter as follows First a rule induction system based on rough sets and attribute-oriented generalization is introduced and was applied to a database of congenital malformation to extract diagnostic rules Then, by the use of the induced knowledge, an expert system which makes a differential diagnosis on congenital disorders is developed Finally, this expert system was evaluated in an outpatient clinic, the results of which show that the system performs as well as a medical expert.