Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.

ABSTRACT:

Introduction

Obesity, defined as a body mass index ≥30 kg/m², is a major public health concern in the United States. Preventative approaches are essential, but they are limited by an inability to accurately predict individuals at highest risk of weight gain. Our objective was to develop accurate weight gain prediction models using the National Institutes of Health All of Us dataset. We hypothesized that machine learning models using both electronic health record and behavioral survey data would outperform models using electronic health record data alone.

Methods

The All of Us dataset was used to identify adults between 18 and 70 ys old with weight measurements 2 y apart between 2008 and 2022. Patients with a history of cancer, bariatric surgery, or pregnancy were

SUBMITTER: Jawara D 

PROVIDER: S-EPMC11911080 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature