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Computational Decision Support System for ADHD Identification

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Abstract

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.

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Acknowledgements

This work was supported by Old Dominion University, Norfolk, Virginia, USA and University of Moratuwa, Sri Lanka. We thank the participants of the system usability study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, 10400, Sri Lanka

    Senuri De Silva, Sanuwani Dayarathna, Gangani Ariyarathne & Dulani Meedeniya

  2. Department of Computer Science, College of Science, Old Dominion University, Norfolk, 23529, USA

    Sampath Jayarathna

  3. Department of Communication Disorders and Special Education, Old Dominion University, Norfolk, 23529, USA

    Anne M. P. Michalek

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  1. Senuri De Silva
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Correspondence to Dulani Meedeniya.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Senuri De Silva is a bachelor student in computer science and engineering at Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka.

Her research interests include biomedical, machine learning and data mining.

Sanuwani Dayarathna is a bachelor student in computer science and engineering at Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka.

Her research interests include biomedical, machine learning and data mining.

Gangani Ariyarathne is a bachelor student in computer science and engineering at Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka.

Her research interests include machine learning, data mining, biomedical, and computer security.

Dulani Meedeniya received the Ph.D. degree in computer science from University of St Andrews, UK in 2013. She is a senior lecturer in Department of Computer Science and Engineering, University of Moratuwa, Sri Lanka. She is a Fellow of Higher Education Academy, UK, Member of The Institution of Engineering and Technology, Member of Institute of Electrical and Electronics Engineers, and a Charted Engineer registered at Engineering Council, UK.

Her research interests include software modelling and design, workflow tool support for bioinformatics, data visualization and recommender systems.

Sampath Jayarathna received the Ph. D. degree in computer science from the Texas A&M University - College Station, USA in 2016. He is an assistant professor in computer science at Old Dominion University, USA, where he is associated with Web Science and Digital Libraries (WS-DL) research group. He is a member of Association for Computing Machinery (ACM), IEEE, and Scientific Research Honor Society (Sigma XI).

His research interests include machine learning, information retrieval, data science, eye tracking, and brain-computer interfacing.

Anne M. P. Michalek received the Ph.D. degree in special education from Old Dominion University, USA in 2012. She is an associate professor of communication disorders and special education at Old Dominion University, USA.

Her research interests include multi-disciplinary approaches using biomedical technologies and instructional tools to improve outcomes for at-risk students and students with ADHD and autism spectrum disorder (ASD).

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De Silva, S., Dayarathna, S., Ariyarathne, G. et al. Computational Decision Support System for ADHD Identification. Int. J. Autom. Comput. 18, 233–255 (2021). https://doi.org/10.1007/s11633-020-1252-1

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  • DOI: https://doi.org/10.1007/s11633-020-1252-1

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