Etude de Mod\`eles \`a base de r\'eseaux Bay\'esiens pour l'aide au   diagnostic de tumeurs c\'er\'ebrales
  This article describes different models based on Bayesian networks RB modeling expertise in the diagnosis of brain tumors. Indeed, they are well adapted to the representation of the uncertainty in the process of diagnosis of these tumors. In our work, we first tested several structures derived from the Bayesian network reasoning performed by doctors on the one hand and structures generated automatically on the other. This step aims to find the best structure that increases diagnostic accuracy. The machine learning algorithms relate MWST-EM algorithms, SEM and SEM + T. To estimate the parameters of the Bayesian network from a database incomplete, we have proposed an extension of the EM algorithm by adding a priori knowledge in the form of the thresholds calculated by the first phase of the algorithm RBE . The very encouraging results obtained are discussed at the end of the paper 
Fradj Ben Lamine (sage), Karim Kalti (sage), Mohamed Ali Mahjoub   (SAGE)
cs.AI