2026-08-20 04:00 UTCOriginal source2 min readUpdated: 2026-08-20 08:50 UTC

A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities

arXiv:2608.18188v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) is a developmental disability characterized by challenges in social interaction and communication. As the causes of ASD remain unclear, identifying relevant features and hidden correlations is crucial for early diagnosis. This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning (ML) techniques to ASD. The primary objective is to examine recent ML applications in ASD research, identifying trends, techniques, and datasets that enhance diagnosis and treatment. Supervised learning methods dominate, as they align well with ASD diagnostic needs; however, the role of deep learning is expanding with greater data availability. Emerging techniques based on hybrid methods, where unsupervised, deep learning, and fuzzy logic could be included, will be interesting to observe in the future. The review highlights key challenges and opportunities, particularly the need for models that can integrate complex data -such as genetic and clinical information- to improve diagnostic accuracy and treatment outcomes. Additionally, incorporating innovative data sources, like wearable devices and biometric sensors, could enable continuous and non-intrusive monitoring, providing a more holistic understanding of ASD. Findings emphasize that addressing current challenges requires interdisciplinary collaboration and expanded datasets tailored to ASD. Future ML models will benefit from broader multimodal data integration, enabling researchers to more comprehensively address the complexities of ASD.

SourcearXiv Machine LearningAuthor: Rafael Mu\~noz-Terol, Jes\'us Peral, Sandra Amador, David Gil

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[Submitted on 18 Aug 2026]

Title:A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities

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Abstract:Autism spectrum disorder (ASD) is a developmental disability characterized by challenges in social interaction and communication. As the causes of ASD remain unclear, identifying relevant features and hidden correlations is crucial for early diagnosis. This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning (ML) techniques to ASD. The primary objective is to examine recent ML applications in ASD research, identifying trends, techniques, and datasets that enhance diagnosis and treatment. Supervised learning methods dominate, as they align well with ASD diagnostic needs; however, the role of deep learning is expanding with greater data availability. Emerging techniques based on hybrid methods, where unsupervised, deep learning, and fuzzy logic could be included, will be interesting to observe in the future. The review highlights key challenges and opportunities, particularly the need for models that can integrate complex data -such as genetic and clinical information- to improve diagnostic accuracy and treatment outcomes. Additionally, incorporating innovative data sources, like wearable devices and biometric sensors, could enable continuous and non-intrusive monitoring, providing a more holistic understanding of ASD. Findings emphasize that addressing current challenges requires interdisciplinary collaboration and expanded datasets tailored to ASD. Future ML models will benefit from broader multimodal data integration, enabling researchers to more comprehensively address the complexities of ASD.

Comments: 17 pages, 8 figures

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

Cite as: arXiv:2608.18188 [cs.LG]

(or arXiv:2608.18188v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.18188

arXiv-issued DOI via DataCite (pending registration)

Journal reference: A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunitie. Heliyon 12(1), e44359, 2026

Related DOI:

https://doi.org/10.1016/j.heliyon.2025.e44359

DOI(s) linking to related resources

Submission history

From: Jesús Peral [view email] [v1] Tue, 18 Aug 2026 11:27:05 UTC (2,720 KB)

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