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While a cure for ASD remains elusive, determining its crucial risk factors and corresponding preventive measures can aid in lessening its incidence and severity. Therefore, the objective of this study is to determine the most significant sociodemographic risk factors associated with ASD among Bangladeshi children and adolescents. In particular, it evaluates the effectiveness of various machine learning classifiers and explores feature selection techniques to identify the most dominant predictors of ASD. To accomplish this, two feature selection strategies\u2014chi\u2010square and F\u2010classify\u2014were applied to extract the most significant features, followed by training eight different machine learning algorithms on full and reduced datasets. Rigorous evaluation using eight distinct metrics revealed that the tree\u2010based algorithm, especially the decision tree, when trained on the F\u2010classify feature subset, exhibited superior performance, achieving the highest accuracy and precision of 99.4%. The most influential features associated with ASD included maternal age, stress during pregnancy, parental illness, family history of ASD, birth asphyxia, and consanguinity.<\/jats:p>","DOI":"10.1155\/acis\/6410913","type":"journal-article","created":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T11:19:58Z","timestamp":1764415198000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Techniques to Uncover Sociodemographic Risk Factors of Autism Spectrum Disorder (ASD) Among Children and Adolescents in Bangladesh"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7444-8067","authenticated-orcid":false,"given":"Md. 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