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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

Mahmoud Elbattah

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
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