Questioning the Benchmarks: Hidden Circularity in AQ-10 Autism Screening Datasets

Autism spectrum disorder (ASD) screening increasingly relies on machine learning models trained on the AQ-10 and Q-CHAT questionnaire datasets, and these models are widely reported to reach 95 to 99 percent accuracy, so confirming whether that accuracy reflects genuine diagnostic learning is vital. This paper examines whether that accuracy reflects genuine diagnostic learning or is simply an artifact of how the datasets are built. The study compares six model families, namely Logistic Regression, Random Forest, Support Vector Machine, XGBoost, FT-Transformer, and TabNet, across three UCI AQ-10

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