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Journal of Autism and Developmental Disorders

Gepubliceerd in:

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 | Uitgave 3/2023

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.

vorige artikel The Impacts of the COVID-19 Pandemic on Therapy Utilization Among Racially/Ethnically and Socio-Economically Diverse Autistic Children

volgende artikel Characterizing Interoceptive Differences in Autism: A Systematic Review and Meta-analysis of Case–control Studies

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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 / Uitgave 3/2023
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-022-05685-x

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