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Attention deficit/hyperactivity disorder(ADHD)is a common disorder among children.ADHD often prevails into adult-hood,unless proper treatments are facilitated to engage self-regulatory systems.Thus,there is a need for effective and reliable mechan-isms for the early identification of ADHD.This paper presents a decision support system for the ADHD identification process.The pro-posed system uses both functional magnetic resonance imaging(fMRI)data and eye movement data.The classification processes con-tain enhanced pipelines,and consist of pre-processing,feature extraction,and feature selection mechanisms.fMRI data are processed by extracting seed-based correlation features in default mode network(DMN)and eye movement data using aggregated features of fixa-tions and saccades.For the classification using eye movement data,an ensemble model is obtained with 81%overall accuracy.For the fMRI classification,a convolutional neural network(CNN)is used with 82%accuracy for the ADHD identification.Both ensemble mod-els are proved for overfitting avoidance.