Prediction of Attention-Deficit/Hyperactivity Disorder Diagnosis Using Brief, Low-Cost Clinical Measures: A Competitive Model Evaluation.

Abstract

Proper diagnosis of ADHD is costly, requiring in-depth evaluation via interview, multi-informant and observational assessment, and scrutiny of possible other conditions. The increasing availability of data may allow the development of machine-learning algorithms capable of accurate diagnostic predictions using low-cost measures to supplement human decision-making. We report on the performance of multiple classification methods used to predict a clinician-consensus ADHD diagnosis. Methods ranged from fairly simple (e.g., logistic regression) to more complex (e.g., random forest), while emphasizing a multi-stage Bayesian approach. Classifiers were evaluated in two large (N>1000), independent cohorts. The multi-stage Bayesian classifier provides an intuitive approach consistent with clinical workflows, and was able to predict expert consensus ADHD diagnosis with high accuracy (>86%)-though not significantly better than other methods. Results suggest that parent and teacher surveys are sufficient for high-confidence classifications in the vast majority of cases, while an important minority require additional evaluation for accurate diagnosis.