A Novel Application for the Efficient and Accessible Diagnosis of ADHD Using Machine Learning (Extended Abstract)

Attention-deficit/hyperactivity disorder is the most pervasive neurodevelopmental disorder among children and adolescents. Current clinical diagnosis, however, is inaccurate, inefficient, and inaccessible in developing nations, hindering the administration of proper treatment regimens. Clinical assessments are based on qualitative observations of perceived behavior. They are time-consuming and costly, preventing minorities and socioeconomically disadvantaged groups from gaining the support they need to succeed academically, socially, and occupationally. A more accurate and accessible method of detection is necessary to ensure that all children are able to be diagnosed and given proper treatment regimens. This research proposes a novel machine learning-based method to analyze pupil-dynamics data as an objective biomarker to characterize ADHD. After visualizing and engineering pupillometric features, a voting ensemble classification algorithm and meta learner were developed and yielded the most optimal leave-one-out-cross-validation metrics on a declassified dataset. The ensemble model, in particular, classified ADHD with. 821 sensitivity, 0.727 specificity, and 0.856 AUROC. This model was implemented in a web application that administers a memory task and captures pupil biometrics in realtime. This application is the first to use pupil-size dynamics as a biomarker, and offers a time-efficient, accurate, and accessible approach to diagnose ADHD in developing nations.