Predicting fine motor deficit in autism by measuring brain activities and characterizing motor impairments

Motor impairments affect approximately 86.9% of children with Autism Spectrum Disorder (ASD), often persisting into adolescence and increasing the risk of Developmental Coordination Disorder (DCD). Despite their prevalence, only 31.6% of affected individuals receive physical therapy, underscoring a critical gap in early intervention. Traditional methods for diagnosing Fine Motor Deficits (FMD) are often time-consuming and costly, necessitating the adoption of data-driven approaches. This study introduces a machine learning framework for the rapid and reliable prediction of fine motor impairments in adolescents with ASD. By integrating EEG-based neurophysiological signals, behavioral assessments, and motor coordination tests, the study evaluates five classification models-Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest, and Neural Network. Among these, Logistic Regression achieved the highest accuracy (95.84%), demonstrating strong predictive power for identifying fine motor deficits. The proposed framework enhances the efficiency of FMD screening and provides an interpretable model for potential clinical use in early ASD diagnosis.