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Early diagnosis of autism spectrum disorder (ASD) is very important for improving autism treatment. Recent studies have investigated early diagnosis of children with ASD using machine learning and eye tracking. This paper presents an eye tracking and pupillary response feature extraction method with a naive Bayes classification model for autism that was tested on the Autism Detection Dataset, a dataset of 25 children with ASD and 50 children with typical development aged 3-6 to identify abnormal pupillary responses in children with autism. The method has an average classification accuracy of 90.67% and an average AUC of 92.24% while using only the pupillary features for modeling, which is better than the 82.2% average accuracy achieved by a pupillary and gaze behavior feature model and 78% average accuracy achieved by a gaze behavior and kinematic feature model. This method is simple and accurate. The results show the effectiveness of this method and the feasibility of real clinical applications of this type for early autism diagnosis based on machine learning and eye tracking.
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