Predicting anorexia nervosa treatment efficacy: an explainable machine learning approach.

ABSTRACT:

Introduction

Anorexia nervosa (AN) is a psychopathology with an alarmingly high mortality rate. The growing number of individuals seeking help, coupled with the limited resources of clinics, highlights the critical need to identify factors that can predict treatment efficacy. Machine learning (ML) techniques hold great promise in this regard. This data-driven approach offers an unbiased means to uncover predictors of specific outcomes, advancing the understanding and management of this challenging condition.

Objective

Six supervised ML algorithms (e.g., Decision Tree and Random Forest) were applied to develop a binary classification model predicting short-term weight recovery/stabilization in AN inpatients and identify the most critical factors influencing this outcome.

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SUBMITTER: Brizzi G 

PROVIDER: S-EPMC12131494 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature