Document Type
Conference Proceeding
Publication Date
12-2017
Abstract
We apply cluster analysis to a sample of 2,116 children with Autism Spectrum Disorder in order to identify patterns of challenging behaviors observed in home and centerbased clinical settings. The largest study of this type to date, and the first to employ machine learning, our results indicate that while the presence of multiple challenging behaviors is common, in most cases a dominant behavior emerges. Furthermore, the trend is also observed when we train our cluster models on the male and female samples separately. This work provides a basis for future studies to understand the relationship of challenging behavior profiles to learning outcomes, with the ultimate goal of providing personalized therapeutic interventions with maximum efficacy and minimum time and cost.
Recommended Citation
E. Stevens, A. Atchison, L. Stevens, E. Hong, D. Granpeesheh, D. Dixon, and E. Linstead. A cluster analysis of challenging behaviors in autism spectrum disorder. In Machine Learning and Applications, 2017. ICMLA ’17, pages 661-666. IEEE, 2017. doi: 10.1109/ICMLA.2017.00-85
Copyright
IEEE
Included in
Behavior and Behavior Mechanisms Commons, Disability and Equity in Education Commons, Other Computer Sciences Commons, Psychological Phenomena and Processes Commons
Comments
This is a pre-copy-editing, author-produced PDF of a paper presented at the 16th IEEE International Conference On Machine Learning And Applications in December 2017. The definitive publisher-authenticated version is available online at DOI: 10.1109/ICMLA.2017.00-85.