ArticleBMC psychiatry2026
Machine learning-based prediction of intellectual disability in children with autism spectrum disorder: using behavioral observation techniques.
Article in BMC psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo construct and evaluate a LightGBM prediction model for intellectual disabilities in children with Autism Spectrum Disorder (ASD).
methodsA total of 384 ASD children who completed the Wechsler Intelligence Test and Adaptive Behavioral Assessment System were included in the analysis. The LightGBM model was trained using behavioral observation data and underwent hyperparameter tuning and feature selection.
resultsAmong the ASD children, 32.9% had comorbid ID. The LightGBM model exhibited the highest sensitivity and accuracy, with values of 0.793 and 0.760, respectively. It also achieved an AUC of 0.747, with overall quality of relationships, unusual sensory interests, and gestures/postures being the top predictive features.
conclusionThe LightGBM model demonstrated strong predictive performance, enabling early identification of comorbid ID in preschool children with ASD and facilitating personalized intervention strategies.
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