• This review was conducted to describe existing research that used supervised machine learning algorithms to analyze large autism datasets.
• Although existing research was limited, various supervised machine learning algorithms have been used to make binary predictions, often based on diagnoses, to aid autism diagnosis and screening efforts, explore the role of genetics in autism, and identify potential autism biomarkers (e.g., using neuroimaging).
• The use of supervised machine learning methods to analyze large collections of autism data is a promising area for future research.
Hyde, K. K., Novack, M. N., LaHaye, N., Parlett-Pelleriti, C., Anden, R., Dixon, D. R., & Linstead, E. (2019). Applications of supervised machine learning in autism spectrum disorder research: A review. Review Journal of Autism and Developmental Disorders, 6(2), 128-146. https://doi.org/10.1007/s40489-019-00158-x