A bayesian CBR applied to the diagnosis of hepatic pathologies: MOCABBAN

Our work presents a case based reasoning system for the diagnosis of hepatic pathologies according to a bayesian model. The main idea consists in a modelling the case base by a bayesian network. Bayesian networks are excellent tools for modelling the uncertainty in terms of their clear graphic representation as well as the conditional probabilities laws defined on a graph. Our network allows a representation of a qualitative and causal knowledge in addition to a quantitative knowledge that expresses the uncertainty. It contains four levels: clinical, biologic, medical imaging and diagnosis-therapy. Each level has a table containing the different conditional probabilities in order to get a good final diagnosis. Each level is composed of a set of attributes; each attribute corresponds to a node of a network. Arcs describe the relationships between attributes as conditional probabilities of attributes in the case. An exact algorithm of inference JLO-Jensen, Lauritzen et Olesen-[1] is used to calculate the conditional probabilities. A new case is introduced in the network and is propagated to the network. If signs are missed, we used another algorithm of learning EM-Expectation Maximisation [2] to estimate the conditional probabilities of the missed variables in order to get a good learning. A good and final diagnosis is then given by the model