Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos
10.7499/j.issn.1008-8830.2309073
- VernacularTitle:不同意图场景眼动注视模式机器学习算法识别孤独症谱系障碍的研究
- Author:
Rong CHENG
1
,
2
,
3
;
Zhong ZHAO
;
Wen-Wen HOU
;
Gang ZHOU
;
Hao-Tian LIAO
;
Xue ZHANG
;
Jing LI
Author Information
1. 中国科学院心理研究所/中国科学院行为科学重点实验室,北京 100101
2. 中国科学院大学心理学系,北京 100049
3. 深圳大学机电与控制工程学院,广东深圳 518010
- Keywords:
Autism spectrum disorder;
Machine learning;
Joint intention;
Action understanding;
Eye tracking;
Child
- From:
Chinese Journal of Contemporary Pediatrics
2024;26(2):151-157
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the differences in visual perception between children with autism spectrum disorder(ASD)and typically developing(TD)children when watching different intention videos,and to explore the feasibility of machine learning algorithms in objectively distinguishing between ASD children and TD children.Methods A total of 58 children with ASD and 50 TD children were enrolled and were asked to watch the videos containing joint intention and non-joint intention,and the gaze duration and frequency in different areas of interest were used as original indicators to construct classifier-based models.The models were evaluated in terms of the indicators such as accuracy,sensitivity,and specificity.Results When using eight common classifiers,including support vector machine,linear discriminant analysis,decision tree,random forest,and K-nearest neighbors(with K values of 1,3,5,and 7),based on the original feature indicators,the highest classification accuracy achieved was 81.90% .A feature reconstruction approach with a decision tree classifier was used to further improve the accuracy of classification,and then the model showed the accuracy of 91.43% ,the specificity of 89.80% ,and the sensitivity of 92.86% ,with an area under the receiver operating characteristic curve of 0.909(P<0.001).Conclusions The machine learning model based on eye-tracking data can accurately distinguish ASD children from TD children,which provides a scientific basis for developing rapid and objective ASD screening tools.[Chinese Journal of Contemporary Pediatrics,2024,26(2):151-157]
