Issam Salman implemented a method for constructing a Tree-Augmented Naive Bayesian (TAN) model and a feature selection method called Selective TAN specifically tailored for incomplete and unbalanced data. He analysed medical records of patients with acute myocardial infarction in both Syria and the Czech Republic. In this analysis, machine learning techniques were used to predict AMI mortality. Finally, he proposed a methodology for learning the structure of BN and Belief Noisy-Or models from incomplete data sets using Gaussian mixtures.