Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data.
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Investigative Techniques Commons, Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Other Computer Engineering Commons, Other Computer Sciences Commons, Other Electrical and Computer Engineering Commons