Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Apr 2, 2021
Date Accepted: Jul 5, 2021
Use of machine learning to classify children with autism and typical development using eye tracking data from face-to-face conversations
ABSTRACT
Background:
Previous eye tracking studies had participants watch images (i.e., picture, video and webpage), and the application of machine learning (ML) on these data showed promising results in identify ASD individuals. Given the fact that gaze behavior differs in face-to-face interaction from image viewing tasks, no study has investigated whether eye tracking data from face-to-face conversations could accurately identify ASD.
Objective:
The objective of this study was to examine whether eye tracking data from face-to-face conversations could classify children with autism spectrum disorder (ASD) and typical development (TD). It was further investigated whether combining features on visual fixation and length of conversation would achieve better classification performance.
Methods:
Both children with ASD and TD were eye-tracked when they were engaged in face-to-face conversations (including 4 conversational sessions) with an interviewer. By implementing forward feature selection, four ML classifiers (support vector machine, SVM; linear discriminant analysis, LDA; decision tree, DT; and random forest, RF) were used to determine the maximum classification accuracy and the corresponding features.
Results:
A maximum classification accuracy of 92.31% was achieved with the SVM classifier by combining features on both visual fixation and session length. The classification accuracy of combined features was higher than using features on visual fixation features (maximum classification accuracy: 84.62%) or session length (maximum classification accuracy: 84.62%) alone.
Conclusions:
Eye tracking data from face-to-face conversations could accurately classify children with ASD and TD, suggesting that ASD might be objectively screened in everyday social interaction. However, future studies need to test on a larger sample of individuals with ASD (with different severity and balanced sex) using data collected from different modalities (e.g., eye tracking, kinematic, EEG, and neuroimaging). In addition, individuals with other clinical conditions (e.g., developmental delay and ADHD) are encouraged to be included in ML studies for detecting ASD.
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