Retrieval and adaptation in CBR through Bayesian Network for diagnosis of hepatic pathologies

Abstract

This article describes a modeling of knowledge for a Case Based Reasoning system (CBR) applied to the diagnosis of the hepatic pathologies, where the cases and the knowledge of the domain are expressed by a Bayesian network (BN). In fact, we are interested in the retrieval and adaptation phases. Th e retrieval phase consists in selecting the most similar case of log linear model by considering the Bayesian Network as a log-linear model based on the simplification of the probabilities. The adaptation phase means modifying solutions of retrieved cases to fit the current problem. The dependence between these two phases is defined by two measures: a similarity measure and an adaptation measure. The objective of this dependence is to guarantee the retrieved case, which is the easiest way to adapt and improve the performance of CBR. An example of the diagnosis of the hepatic pathologies will illustrate the proposed approach.