Journal
JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS
Volume 53, Issue 3, Pages 934-946Publisher
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1007/s10803-022-05685-x
Keywords
Autism; Behavior segmentation; Entropy; Eye-tracking; Machine learning; Oculomotor
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This study utilized a machine learning approach to analyze the gaze behavior time series data in face-to-face conversations between children with autism spectrum disorder (ASD) and typically developing children. By combining behavior segmentation and a threshold classifier, the study achieved a high accuracy rate in automated screening of ASD.
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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