Enhancing the Effectiveness of Attention-Deficit/Hyperactivity Disorder Screening Using the SNAP-IV: A Deep Learning Approach.
TL;DR
A multi-algorithm machine-learning framework was applied to refine the 18 core ADHD items of the SNAP-IV by identifying the most predictive items through cross-model consensus ranking while preserving balanced symptom construct representation.
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition requiring early and accurate identification to optimize outcomes. The Swanson, Nolan, and Pelham Rating Scale (SNAP-IV) is widely used to assess ADHD symptoms; however, its length may limit feasibility in large-scale screening. This study applied a multi-algorithm machine-learning framework to refine the 18 core ADHD items of the SNAP-IV by identifying the most predictive items through cross-model consensus ranking while preserving balanced symptom construct representation. Data were drawn from the Taiwan National Epidemiological Study of Child Mental Disorders (410 ADHD, 3,607 controls) and an independent National Taiwan University Hospital cohort (676 ADHD, 374 controls). Across ten classifiers optimized for screening with priority on sensitivity, reduced subsets comprising 4 parent-reported and 6 teacher-reported items retained robust predictive performance across cohorts. Confirmatory factor analysis supported the structural validity of the two-factor (Inattention/Hyperactivity-Impulsivity) shortened scales, with strong latent reliability (McDonald's omega). A machine learning-derived, construct-balanced SNAP-IV short form provides an efficient and psychometrically sound tool for ADHD screening.