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Computational Decision Support System for ADHD Identification
Senuri De Silva1
发表期刊International Journal of Automation and Computing
ISSN1476-8186
2021
卷号18期号:2页码:233-255
摘要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.
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)
DOI10.1007/s11633-020-1252-1
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被引频次:8[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/44019
专题学术期刊_Machine Intelligence Research
作者单位1.Department of Computer Science and Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka
2.Department of Computer Science, College of Science, Old Dominion University, Norfolk 23529, USA
3.Department of Communication Disorders and Special Education, Old Dominion University, Norfolk 23529, USA
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Senuri De Silva. Computational Decision Support System for ADHD Identification[J]. International Journal of Automation and Computing,2021,18(2):233-255.
APA Senuri De Silva.(2021).Computational Decision Support System for ADHD Identification.International Journal of Automation and Computing,18(2),233-255.
MLA Senuri De Silva."Computational Decision Support System for ADHD Identification".International Journal of Automation and Computing 18.2(2021):233-255.
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