aceEstablishing Diagnosis Models for Autism Spectrum Disorder With Auditory Brainstem Response Using Machine Learning Methods
This study aimed to develop and validate models built using machine learning methods for the diagnosis of autism spectrum disorder (ASD). A total of 851 children with suspected ASD were enrolled. Related features were acquired using the Autism Behavior Checklist and Auditory Brainstem Response (ABR) outcomes. The Shapley additive explanation (SHAP) values were used to explain the feature impact used in the models with the best performance. In addition, different methods were used to depict group differences. Our…
Machine learning models combining Autism Behavior Checklist and auditory brainstem response features could classify children with suspected autism, with XGBoost reaching 85.9% accuracy and Waves III and V as the strongest features.
The source describes the pathway link as something that 'may be associated' with autistic behaviors, and the AUROC of 0.779 indicates only moderate discrimination, so the findings do not establish clinical readiness.
Evidence
- Peer-reviewedAutism Research2026-10-16
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Truvace Impact Record TRV-2026-1951, v1: “Establishing Diagnosis Models for Autism Spectrum Disorder With Auditory Brainstem Response Using Machine Learning Methods.” Truvace, 2026-10-19. /record/TRV-2026-1951 (accessed at citation time). sha256 906c81aff5fa0bc3…
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