Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach
Saved in:
| 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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
