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Precise and interpretable classification of autism-related behaviors is important
for initial diagnosis, personalized intervention, and support arrangements. This study
proposes an interpretable machine learning (ML) model using Light Gradient
Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) to classify
behavioral patterns into four categories (normal, mild, moderate, and severe)
associated with Autism Spectrum Disorder (ASD) based on a custom 377-instance
survey dataset from Iraqi parents and teachers of children aged 6-12. The model
observes 16 key features across communication and social interaction, repetitive
behaviors, language, and adaptive skills, preprocessed via interquartile range (IQR)
outlier removal, me
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