Less is more? A hybrid machine learning and psychometric approach to identifying clinically relevant psychopathology in Chinese youth using the Child Behaviour Checklist

OBJECTIVE: Screening for psychiatric risk in youth at the population level is often constrained by resource limitations and lengthy assessment tools. This study aimed to develop a reduced, psychometrically robust subset of Child Behavior Checklist (CBCL) items to effectively predict transdiagnostic psychiatric morbidity in the youth population. METHODS: Data were drawn from a nationally representative sample of 72,109 Chinese youth aged 6-16 years. Initial item screening on the full sample employed unique variance analysis, item-rest correlation tests, and conceptual redundancy checks. A nested case-control subset (approximately 4,500 with diagnoses and 5,000 without) was used for feature selection. Recursive feature elimination with repeated cross-validation was then applied to the nested subset to derive three item sets (n = 35, 69, 98). These were psychometrically evaluated using exploratory graph analysis and confirmatory factor analysis in two age- and gender-stratified samples from the full dataset. Predictive performance was assessed using five machine learning algorithms, trained and tested on a 70/30 split of the nested case-control data. RESULTS: The 35-item and 60-item subsets achieved high diagnostic accuracy (AUC = 0.88-0.89), with performance comparable to the best-performing larger subsets. Items captured transdiagnostic dimensions including Functional Somatic Symptoms, Neurodevelopmental Dysregulation, Affective-Social Withdrawal, Threat Sensitivity and Cognitive-Perceptual Disturbance, and Disinhibited-Irritable Externalising. CONCLUSIONS: The reduced CBCL sets demonstrated strong diagnostic utility and psychometric soundness. This scalable tool supports transdiagnostic, data-driven screening of youth psychiatric risk at the population level.