Development of Bayesian Network models for obstructive sleep apnea syndrome assessment

Bayesian Belief networks have been used for diagnosis in some medical domains and in this thesis we provide a methodology for creating Bayesian Networks to predict Obstructive Sleep Apnea Syndrome severity. We build 3 Bayesian Network topollogies: by knowledge engineering, Nalve Bayes configuration and a third topology is created using results of the Nalve network. All networks are trained on data from 652 patients referred for an overnight polysomnogram. Data is derived from multiple data sources and includes a mix of continuous and discrete variables. We investigate the impact of different topologies and discretizing continuous variables, adding nodes with large amounts of missing values, and removing nodes from networks. Results show that performance is dependent on the interaction between topology and discretization. Node removal. increases sensitivity while node addition decrealses it.