Machine Learning‐Based Early Prediction Model for Autism Spectrum Disorder in Infants Using Acoustic Feature

This study aimed to create a machine learning-based predictive model for early detection of autism spectrum disorder (ASD) in infants using acoustic features. Conducted as a prospective cohort at Nanjing Medical University from 2019 to 2024, infants aged 9-18 months from an ASD sibling cohort participated. Behavioral and vocalization data were gathered during the Still-Face Paradigm, with ASD diagnoses confirmed at 36 months through ADOS and ADI-R assessments. Researchers extracted 4368 acoustic features from the recordings and applied LASSO regression for dimensionality reduction, identifying 39 key features. A support vector machine (SVM) classifier was then developed, tested with four kernel functions-linear, radial basis function, polynomial, and sigmoid-via tenfold cross-validation. The final sample included 88 infants, 28 of whom were diagnosed with ASD. The sigmoid kernel yielded the best results, achieving a 92.86% sensitivity, 93.33% specificity, and a 93.18% accuracy. Notably, spectral and energy-related features were significantly higher in ASD infants (p < 0.01). These findings suggest that acoustic features can serve as early, noninvasive biomarkers for ASD, and the SVM model demonstrates significant promise for early screening and intervention efforts.