Abstract
Early detection of autism spectrum disorder (ASD) is highly beneficial to the health sustainability of children. Existing detection methods depend on the assessment of experts, which are subjective and costly. In this study, we proposed a machine learning approach that fuses physiological data (electroencephalography, EEG) and behavioral data (eye fixation and facial expression) to detect children with ASD. Its implementation can improve detection efficiency and reduce costs. First, we used an innovative approach to extract features of eye fixation, facial expression, and EEG data. Then, a hybrid fusion approach based on a weighted naive Bayes algorithm was presented for multimodal data fusion with a classification accuracy of 87.50%. Results suggest that the machine learning classification approach in this study is effective for the early detection of ASD. Confusion matrices and graphs demonstrate that eye fixation, facial expression, and EEG have different discriminative powers for the detection of ASD and typically developing children, and EEG may be the most discriminative information. The physiological and behavioral data have important complementary characteristics. Thus, the machine learning approach proposed in this study, which combines the complementary information, can significantly improve classification accuracy.