Discovery of approximate medical knowledge based on rough set model

One of the most important problems on rule induction methods is that extracted rules do not plausibly represent information on experts' decision processes, which makes rule interpretation by domain experts difficult. This paper first discusses the characteristics of medical reasoning and defines positive and negative rules which models medical experts' rules. Then, algorithms for induction of positive and negative rules are introduced. The proposed 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.