Abstract
This study segmented the time series of gaze behavior from nineteen children with autism spectrum disorder (ASD) and 20 children with typical development in a face-to-face conversation. A machine learning approach showed that behavior segments produced by these two groups of participants could be classified with the highest accuracy of 74.15%. These results were further used to classify children using a threshold classifier. A maximum classification accuracy of 87.18% was achieved, under the condition that a participant was considered as ‘ASD’ if over 46% of the child’s 7-s behavior segments were classified as ASD-like behaviors. The idea of combining the behavior segmentation technique and the threshold classifier could maximally preserve participants’ data, and promote the automatic screening of ASD.
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Abbreviations
- ADHD:
-
Attention-deficit hyperactivity disorder
- ADI-R:
-
Autism diagnostic interview-revised
- ADOS:
-
Autism diagnostic observation schedule
- ASD:
-
Autism spectrum disorder
- DSM-V:
-
The diagnostic and statistical manual of mental disorders—5th Edition
- EEG:
-
Electroencephalography
- ML:
-
Machine learning
- TD:
-
Typical development
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Funding
The study was financially supported by the National Natural Science Foundation of China (Grant No. 82171539), the SZU funding project (Grant No. 860-000002110259), the Science and Technology Innovation Committee of Shenzhen (Grant No. JCYJ20190808115205498), the Key medical discipline of GuangMing Shenzhen (Grant No. 12 Epidemiology), Sanming project of medicine in Shenzhen (Grant No. SZSM201612079), Key Realm R&D Program of Guangdong Province (Grant No. 2019B030335001), Shenzhen Key Medical Discipline Construction Fund (Grant No. SZXK042), and Shenzhen Double Chain Grant [2018]256.
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ZZ, XZ, XQ, and JL designed the experiment and recruited participants. ZZ, JW, JX, and XH performed data analysis. ZZ, JW, XZ, and XQ drafted and revised the manuscript.
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Zhao, Z., Wei, J., Xing, J. et al. Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach. J Autism Dev Disord 53, 934–946 (2023). https://doi.org/10.1007/s10803-022-05685-x
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DOI: https://doi.org/10.1007/s10803-022-05685-x