A Rule-Based System for ADHD Identification using Eye Movement Data

Attention Deficit Hyperactivity Disorder (ADHD) is one of the common psychiatric disorder in childhood, which can continue to adulthood. The ADHD diagnosed population has been increasing, causing a negative impact on their families and society. This paper addresses the effective identification of ADHD in early stages. We have used a rulebased approach to analyse the accuracies of decision tree classifiers in identifying ADHD subjects. The dataset consists of eye movements and eye positions of different gaze event types. The feature extraction process considers fixations, saccades, gaze positions, and pupil diameters. The decision tree-based algorithms have shown a maximum accuracy of 84% and classification rule algorithms have shown an accuracy of 82% using eye movement measurements. Thus, both algorithms have shown high accuracy with the rule-based component.