Date of Award

Summer 8-2020

Document Type


Degree Name

Doctor of Philosophy (PhD)


Computational and Data Sciences

First Advisor

Dr. Erik Linstead

Second Advisor

Dr. Adrian Vajiac

Third Advisor

Dr. Dave Frederick


Over the past 100 years, assessment tools have been developed that allow us to explore mental and behavioral processes that could not be measured before. However, conventional statistical models used for psychological data are lacking in thoroughness and predictability. This provides a perfect opportunity to use machine learning to study the data in a novel way. In this paper, we present examples of using machine learning techniques with data in three areas: eating disorders, body satisfaction, and Autism Spectrum Disorder (ASD). We explore clustering algorithms as well as virtual reality (VR).

Our first study employs the k-means clustering algorithm to explore eating disorder behaviors. Our results show that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) are good predictors of eating disorder behavior. Our second study uses a hierarchical clustering algorithm to find patterns in the dataset that were previously not considered. We found four clusters that may highlight the unique differences between participants who had positive body image versus participants who had negative body image. The final chapter presents a case study with a specific VR tool, Bob’s Fish Shop, and users with ASD and Attention Deficit Hyperactivity Disorder (ADHD). We hypothesize that, through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding.

Through the implementation of various machine learning algorithms and programs, we can study the human experience in a way that has never been done. We can account for neurodiverse populations and assist them in ways that are not only helpful but also educational.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.