Using Bayesian Methods for Predicting the Development of Children Autism

In this paper, we propose a methodology for using static Bayesian networks (BN) in the tasks of predicting the development of the children's disease “autism” aged 4 to 11 years. Methods of constructing the structure of a static BN, parametric learning, validation are considered, a sensitivity analysis and scenario analysis of “What-if' are carried out. The model was designed in collaboration with pediatricians, as well as subject matter experts in the selection and quantification of input and output variables.