Machine learning v. traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial

Background While effective treatments exist for binge-eating disorder (BED), prediction of treatment outcomes has proven difficult, and few reliable predictors have been identified. Machine learning is a promising method for improving the accuracy of difficult-to-predict outcomes. We compared the accuracy of traditional and machine-learning approaches for predicting BED treatment outcomes. Methods Participants were 191 adults with BED in a randomized controlled trial testing 6-month behavioral and stepped-care treatments.