Publication date: Jan 08, 2026
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
| Concepts | Keywords |
|---|---|
| Acoustic | acoustic features |
| Autism | autism spectrum disorder |
| Nanjing | machine learning |
| Tenfold | model |
| support vector machine |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | Autism Spectrum Disorder |
| disease | MESH | Face |
| disease | MESH | included |