Prediction of ADHD 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. We report on the performance of multiple classification methods used to predict a clinician-consensus ADHD diagnosis. Classification methods ranged from fairly simple (e.g., logistic regression) to more complex (e.g., random forest), and also included a multi-stage Bayesian approach. All methods were evaluated in two large (N>1000), independent cohorts. The multi-stage Bayesian classifier provides an intuitive approach that is consistent with clinical workflows, and is able to predict ADHD diagnosis with high accuracy (>86%)—though not significantly better than other commonly used classifiers, including logistic regression. Results suggest that data from parent and teacher surveys is sufficient for high-confidence classifications in the vast majority of cases using relatively straightforward methods.