Identifying children with autism spectrum disorder based on their face processing abnormality: a machine learning framework
Abstract
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. Autism Res 2016, 9: 888–898 . © 2016 International Society for Autism Research, Wiley Periodicals, Inc.
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Higher performance has been reported for behavior when constructing a model using visual face scanning (88.1%) or goal-oriented reach (96.7%) measures.
“Higher performance has been reported for behavior when constructing a model using visual face scanning (88.1%; 92) or goal-oriented reach (96.7%; 93) measures.”
From Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm (2018) · cites this paper as [92]
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- Authors
- Liu, W., Li, M., & Yi, L.
- Affiliations
- Sun Yat-sen University (CN)
- Peking University (CN)
- Corresponding author
- Li, Ming
- Yi, Li
- Venue
- Autism Research
- Publisher
- Wiley
- Published
- 2016-04
- Volume
- 9
- Issue
- 8
- Pages
- 888-898
- ISSN
- 1939-3792 (ISSN-L)
- 1939-3806
- OpenAlex
- W2328587688
- Semantic Scholar
- 05a9d8caa91ac2aa7641c96a4218c1eed5f4684f
- Cited by
- 297
- References
- 36
- Citations by year
- 2026: 19 · 2025: 33 · 2024: 50 · 2023: 35 · 2022: 37 · 2021: 45 · 2020: 33 · 2019: 26 · 2018: 11 · 2017: 8
- FWCI
- 9.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Autism Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (31571135, 31200779, 61401524)
- Science and Technology Foundation of Guizhou Province (2014A030313123)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- autism spectrum disorder, face processing, eye movement patterns, facial scanning, ASD classification, children, early diagnosis, machine learning classification
- MeSH terms
- Autism Spectrum Disorder, Machine Learning, Algorithms, Child, Child, Preschool, China, Eye Movements, Face, Female, Humans, Male, Sensitivity and Specificity, Visual Perception, Reproducibility of Results, Recognition, Psychology
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