Machine learning-based prediction of intellectual disability in children with autism spectrum disorder using behavioral observation

Researchers developed a LightGBM predictive model using behavioral observation data from 384 children with ASD to identify comorbid intellectual disability, achieving 76% accuracy and 79.3% sensitivity. The model identified quality of relationships, unusual sensory interests, and gesture patterns as top predictive features, enabling early intervention planning.