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 sen...

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Bibliographic Details
Published in2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT) pp. 525 - 529
Main Authors Voronenko, Mariia, Lurie, Iryna, Boskin, Oleg, Zhunissova, Ulzhalgas, Baranenko, Roman, Lytvynenko, Volodymyr
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
Autism spectrum disorder
Bayesian Networks
Parametric learning
Sensitivity analysis
Structural learning
Validation
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DOI10.1109/ATIT49449.2019.9030523

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Summary: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.
DOI:10.1109/ATIT49449.2019.9030523