Discovering Knowledge from Medical Databases

We investigate new approaches for knowledge discovery from two medical databases. Two different kinds of knowledge, namely rules and causal structures, are learned. Rules capture interesting patterns and regularities in the database. Causal structures represented by Bayesian networks capture the causality relationships among the attributes. We employ advanced evolutionary algorithms for these discovery tasks. In particular, Generic Genetic Programming is employed as rule learning algorithm. Our approach for discovering causality relationships is based on Evolutionary Programming which learns Bayesian network structures.