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

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Bibliographic Details
Title: Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach
Language: English
Authors: Zhao, Zhong, Wei, Jiwei, Xing, Jiayi, Zhang, Xiaobin, Qu, Xingda, Hu, Xinyao, Lu, Jianping
Source: Journal of Autism and Developmental Disorders. Mar 2023 53(3):934-946.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 13
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Descriptors: Children, Autism Spectrum Disorders, Symptoms (Individual Disorders), Eye Movements, Interpersonal Communication, Classification, Accuracy, Disability Identification
DOI: 10.1007/s10803-022-05685-x
ISSN: 0162-3257
1573-3432
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.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1368763
Database: ERIC
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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.
ISSN:0162-3257
1573-3432
DOI:10.1007/s10803-022-05685-x