journal article Mar 30, 2026

Machine Learning Algorithms for Detection of Autism Spectrum Disorders in Early Childhood: A Scoping Review

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Abstract
ABSTRACT

Objective
This scoping review assesses machine learning (ML)‐based prediction models for autism spectrum disorder (ASD) in early childhood, with the aim of providing a technical and conceptual foundation for improving early ASD detection.


Methods
Relevant studies on ML‐driven ASD prediction models were systematically retrieved from eight databases: PubMed, Embase, Web of Science Core Collection, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Biomedical Database (CBM), Wanfang Data Knowledge Service Platform (WF), and VIP Chinese Science and Technology Journal Database. The scoping review methodology was strictly followed for data extraction and analysis.


Results
A total of 16 studies focusing on the application of diverse machine learning algorithms for ASD identification and prediction were included. Among these, 4 studies (25%) employed multiple algorithms for predictive modeling. The most frequently utilized algorithms were tree‐based methods (7 studies, 44%), neural networks (NNs) (7 studies, 44%), support vector machines (SVMs) (5 studies, 31%), and regularized logistic regression (3 studies, 19%). Twelve studies (75%) reported Area Under the Curve (AUC) values, all exceeding the 0.7 threshold. Notably, 7 studies (44%) achieved excellent predictive performance with AUC values surpassing 0.9.


Conclusions
ML‐based models hold substantial promise for the early identification of ASD, which is critical for improving patient outcomes. Future research should focus on standardizing ML model frameworks, refining theoretical underpinnings to enhance practical applicability, and promoting clinical implementation following rigorous validation. These efforts will further enhance the accuracy and utility of such predictive models.
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References
55
[5]
Anurekha G. "Performance Analysis of Supervised Approaches for Autism Spectrum Disorder Detection" International Journal of Trend in Research and Development (2017)
[33]
Automated identification of postural control for children with autism spectrum disorder using a machine learning approach

Yumeng Li, Melissa A. Mache, Teri A. Todd

Journal of Biomechanics 10.1016/j.jbiomech.2020.110073
[37]
Mahoney W. J. M.Villacrusis M.Sompolski et al. (2021) Nursing Care for Pediatric Patients With Autism Spectrum Disorders: A Cross‐sectional Survey of Perceptions and Strategies https://doi.org/10.1111/jspn.12332. 10.1111/jspn.12332
[42]
Publishing American Psychiatric. (2013) Diagnostic and Statistical Manual of Mental Disorders 5e. 10.1176/appi.books.9780890425596
[49]
Walker‐Andrews A. S. "Emotions and Social Development: Infants’ Recognition of Emotions in Others" Pediatrics (1998) 10.1542/peds.102.se1.1268

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Details
Published
Mar 30, 2026
Vol/Issue
39(2)
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Funding
Tianjin Municipal Health Commission Award: 2023022
Cite This Article
Xiaoling Gu, Jinghan Jia, Xinrui Zhang, et al. (2026). Machine Learning Algorithms for Detection of Autism Spectrum Disorders in Early Childhood: A Scoping Review. Journal of Child and Adolescent Psychiatric Nursing, 39(2). https://doi.org/10.1111/jcap.70052