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- Title
Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach.
- Authors
Zhao, Zhong; Wei, Jiwei; Xing, Jiayi; Zhang, Xiaobin; Qu, Xingda; Hu, Xinyao; Lu, Jianping
- 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.
- Subjects
EYE movements; CHILD development; MACHINE learning; CHILDREN with disabilities; COMPARATIVE studies; HUMAN services programs; AUTISM; DESCRIPTIVE statistics; RESEARCH funding; EYE muscles; CHILDREN
- Publication
Journal of Autism & Developmental Disorders, 2023, Vol 53, Issue 3, p934
- ISSN
0162-3257
- Publication type
Article
- DOI
10.1007/s10803-022-05685-x