Using Bayesian Methods for Predicting the Development of Children Autism - Voronenko, Mariia; Lurie, Iryna; Boskin, Oleg; Zhunissova, Ulzhalgas; Baranenko, Roman; Lytvynenko, Volodymyr (2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT), 2019)
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.