[article] 
inJournal of Autism and Developmental Disorders > 53-3 (March 2023) . - p.934-946

Titre :	Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach
Type de document :	Texte imprimé et/ou numérique
Auteurs :	Zhong ZHAO, Auteur ; Jiwei WEI, Auteur ; Jiayi XING, Auteur ; Xiaobin ZHANG, Auteur ; Xingda QU, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur
Article en page(s) :	p.934-946
Langues :	Anglais (eng)
Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	https://doi.org/10.1007/s10803-022-05685-x
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=500

[article] Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach [Texte imprimé et/ou numérique] / Zhong ZHAO, Auteur ; Jiwei WEI, Auteur ; Jiayi XING, Auteur ; Xiaobin ZHANG, Auteur ; Xingda QU, Auteur ; Xinyao HU, Auteur ; Jianping LU, Auteur . - p.934-946.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 53-3 (March 2023) . - p.934-946

Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	https://doi.org/10.1007/s10803-022-05685-x
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=500