Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of unsupervised machine learning in ASD research and provide insight into the types of questions being answered with these methods.
Parlett-Pelleriti, C.M., Stevens, E., Dixon, D. et al. Applications of Unsupervised Machine Learning in Autism Spectrum Disorder Research: a Review. Rev J Autism Dev Disord (2022). https://doi.org/10.1007/s40489-021-00299-y
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