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Computational Decision Support System for ADHD Identification 被引量:2
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作者 Senuri De Silva Sanuwani Dayarathna +3 位作者 Gangani Ariyarathne Dulani Meedeniya Sampath Jayarathna Anne M.P.Michalek 《International Journal of Automation and computing》 EI CSCD 2021年第2期233-255,共23页
Attention deficit/hyperactivity disorder(ADHD)is a common disorder among children.ADHD often prevails into adulthood,unless proper treatments are facilitated to engage self-regulatory systems.Thus,there is a need for ... Attention deficit/hyperactivity disorder(ADHD)is a common disorder among children.ADHD often prevails into adulthood,unless proper treatments are facilitated to engage self-regulatory systems.Thus,there is a need for effective and reliable mechanisms for the early identification of ADHD.This paper presents a decision support system for the ADHD identification process.The proposed system uses both functional magnetic resonance imaging(fMRI)data and eye movement data.The classification processes contain 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 fixations 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 models are proved for overfitting avoidance. 展开更多
关键词 Attention deficit/hyperactivity disorder(ADHD) functional magnetic resonance imaging(fMRI) eye movement data seed-based correlation ensembled model convolutional neural network(CNN) default mode network(DMN) SACCADES FIXATIONS ADHD-Care decision support system(DDS)
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