Binge Eating, Purging, and Restriction Symptoms: Increasing Accuracy of Prediction Using Machine Learning

Abstract

Eating disorders are severe mental illnesses characterized by the hallmark behaviors of binge eating, restriction, and purging. These disordered eating behaviors carry extreme impairment and medical complications, regardless of eating disorder diagnosis. Despite the importance of these disordered behaviors to every eating disorder diagnosis, our current models are not able to accurately predict behavior occurrence. The current study utilized machine learning to develop longitudinal predictive models of binge eating, purging, and restriction in an eating disorder sample (N = 60) using real-time intensive longitudinal data. Participants completed four daily assessments of eating disorder symptoms and emotions for 25 days on a smartphone (total data points per participant = 100). Using data, we were able to compute highly accurate prediction models for binge eating, restriction, and purging (.76-.96 accuracy). The ability to accurately predict the occurrence of binge eating, restriction, and purging has crucial implications for the development of preventative interventions for the eating disorders. Machine learning models may be able to accurately predict onset of problematic psychiatric behaviors leading to preventative interventions designed to disrupt engagement in such behaviors.

Keywords: binge eating; eating disorders; machine-learning; purging; restriction.