Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10.

ABSTRACT: Early identification improves life outcomes for individuals with autism. This study addresses a central question: do compact subsets of the most predictive QCHAT-10 items, when fed into machine learning (ML) models trained to reproduce the full questionnaire's screening result, generalize to predicting clinician-established autism diagnoses in independent clinical settings? We applied ML to the 10-question QCHAT-10, training models on New Zealand (n = 1054) and Saudi Arabian (n = 506) datasets with QCHAT-derived labels and testing on Polish data with clinical diagnoses (n = 252). Recursive Feature Elimination identified four-item models retaining three common features: eye contact, following gaze direction, and pretend play. When tested on clinically-diagnosed Polish cases at the 0.3 predi

SUBMITTER: Sollis LJ 

PROVIDER: S-EPMC12594979 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature