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

Lauren N. Forrest, Valentina Ivezaj, Carlos M. Grilo

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)2777-2788
Number of pages12
JournalPsychological medicine
Volume53
Issue number7
DOIs
StatePublished - May 25 2023

All Science Journal Classification (ASJC) codes

  • Applied Psychology
  • Psychiatry and Mental health

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