Validating the AQ-10 Autism Screening Threshold Using Machine Learning Models

Autism Spectrum Disorder (ASD) is a lifelong developmental condition for which early identification is essential. The Autism Quotient-10 (AQ-10) is frequently used as a quick screening instrument, classifying individuals scoring seven or more as potentially autistic. This study applies machine learning (ML) models to a public AQ-10 dataset to determine whether predictive algorithms can provide insights beyond the rule-based threshold. Logistic Regression and Random Forest classifiers were built, validated, and compared. Both models demonstrated perfect scores on all evaluation metrics, reflecting the deterministic nature of the dataset rather than offering new diagnostic information. Demographic examination revealed meaningful differences in screening patterns across gender, age groups, ethnicity, and family history of ASD. These findings emphasize that ML models trained on threshold-derived labels cannot infer clinical patterns beyond the embedded scoring rule. Future work will require clinician-confirmed datasets and multimodal features to meaningfully advance computational autism screening.