Journal
AUTISM RESEARCH
Volume 9, Issue 8, Pages 888-898Publisher
WILEY
DOI: 10.1002/aur.1615
Keywords
autism spectrum disorder; face processing; eye tracking; machine learning
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Funding
- National Natural Science Foundation of China [31571135, 31200779, 61401524]
- National Science Foundation of Guangdong Province [2014A030313123]
- Fundamental Research Funds for the Central Universities [15lgjc12, 15lgjc40]
- Guangdong Shunde SYSU-CMU Joint Research Institute [20140302]
- CMU-SYSU Collaborative Innovation Research Center
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The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. (C) 2016 International Society for Autism Research, Wiley Periodicals, Inc.
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