Data‐Driven and Explainable Discovery of Autism Spectrum Disorder Patterns Across Age Groups

Autism Spectrum Disorder (ASD) presents a complex and diverse challenge, with difficulties in social communication and restricted interests manifesting differently among children, adolescents and adults. The traditional ASD diagnosis is often time consuming and resource intensive, delaying access to crucial interventions. Early detection of ASD is vital, as it enables timely referral for comprehensive assessment and supports interventions during key developmental periods, greatly enhancing long‐term outcomes. To address these challenges, this research presents three complementary studies examining ASD detection across children, adolescents and adults. Using the Autism Spectrum Quotient (AQ) short‐form dataset encompassing behavioural, communication and self‐reported indicators, each study applies decision tree machine learning models to identify behavioural screening patterns within each age group. Study 1 focuses on children, highlighting observable social and behavioural cues; Study 2 investigates adolescents, revealing transitional communication and self‐awareness patterns; and Study 3 explores adults, emphasising subtle self‐reported and behavioural markers. Across studies, interpretable models achieved strong classification accuracy while revealing developmental variations in ASD‐related traits. Collectively, these findings enhance understanding of age‐specific patterns in ASD expression, support earlier and more individualised screening and provide a foundation for designing tailored interventions that align with developmental milestones and improve overall care quality.