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01-08-2022 | Original Paper

Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach

Auteurs: Zhong Zhao, Jiwei Wei, Jiayi Xing, Xiaobin Zhang, Xingda Qu, Xinyao Hu, Jianping Lu

Gepubliceerd in: Journal of Autism and Developmental Disorders

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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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Metagegevens
Titel
Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach
Auteurs
Zhong Zhao
Jiwei Wei
Jiayi Xing
Xiaobin Zhang
Xingda Qu
Xinyao Hu
Jianping Lu
Publicatiedatum
01-08-2022
Uitgeverij
Springer US
Gepubliceerd in
Journal of Autism and Developmental Disorders
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI
https://doi.org/10.1007/s10803-022-05685-x