Mahmoud Elbattah
Applications of machine learning methods to assist the diagnosis of autism spectrum disorder
Elbattah, Mahmoud; Carette, Romuald; Cilia, Federica; Guérin, Jean-Luc; Dequen, Gilles
Authors
Romuald Carette
Federica Cilia
Jean-Luc Guérin
Gilles Dequen
Abstract
Autism spectrum disorder (ASD) is a lifelong neuro-developmental disorder that is generally marked by a set of communication and social impairments. The early diagnosis of autism is genuinely beneficial for the welfare of children and parents as well. However, making an accurate diagnosis of autism remains a challenging task, which requires an intensive clinical assessment. The lack of a gold standard test calls for developing assistive instruments to support the process of examination and diagnosis. In this respect, this chapter seeks to provide practical applications of machine learning (ML) for that purpose. The study stemmed from an interdisciplinary collaboration by joint efforts of psychology and artificial intelligence researchers. The chapter is structured into two main parts as follows. Initially, the first part provides a review of the literature that approached the ASD diagnosis using a variety of ML approaches. Subsequently, the chapter presents a set of empirical ML experiments using an eye-tracking dataset. A vision-based approach is adopted based on the visual representation of eye-tracking scanpaths as a form for learning the behavioral patterns of gaze. The ML experiments include the application of supervised and unsupervised learning. It is practically demonstrated how ML could effectively support the ASD diagnosis through providing a data-driven second opinion.
Citation
Elbattah, M., Carette, R., Cilia, F., Guérin, J., & Dequen, G. (2023). Applications of machine learning methods to assist the diagnosis of autism spectrum disorder. In Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2: Diagnosis and Clinical Analysis (99-119). Elsevier. https://doi.org/10.1016/B978-0-12-824421-0.00013-8
Acceptance Date | Jul 20, 2021 |
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Online Publication Date | Jan 27, 2023 |
Publication Date | 2023 |
Deposit Date | Jan 31, 2023 |
Publicly Available Date | Mar 28, 2024 |
Publisher | Elsevier |
Pages | 99-119 |
Book Title | Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2: Diagnosis and Clinical Analysis |
Chapter Number | 5 |
ISBN | 9780128244210 |
DOI | https://doi.org/10.1016/B978-0-12-824421-0.00013-8 |
Keywords | ASD; Autism spectrum disorder; Neural Engineering; Machine learning, Diagnosis, Diagnostics |
Public URL | https://uwe-repository.worktribe.com/output/10392528 |
Publisher URL | https://www.sciencedirect.com/science/article/pii/B9780128244210000138?via%3Dihub |
Related Public URLs | https://www.sciencedirect.com/book/9780128244210/neural-engineering-techniques-for-autism-spectrum-disorder-volume-2 |
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