Integrating Genetic, Environmental, Cognitive, and Temperament Data for ADHD Prediction in Explainable Deep Learning Models

Objective: Attention-deficit/hyperactivity disorder (ADHD) is clinically and etiologically heterogeneous, and diagnostic decisions may benefit from integrating multiple sources of information. We developed an explainable deep learning approach to test whether genetic, environmental, cognitive, demographic, and temperament data could classify ADHD diagnosis and identify features contributing to model decisions. Method: We analyzed participants from the Oregon ADHD-1000 cohort split into training, validation, and test subsets. We trained modular neural network models classifying ADHD case-control status using SNP-level genotype data with biological annotations, polygenic scores, demographics,