Identifying individuals at risk for weight gain using machine learning in electronic medical records from the United States.

ABSTRACT:

Aims

Numerous risk factors for the development of obesity have been identified, yet the aetiology is not well understood. Traditional statistical methods for analysing observational data are limited by the volume and characteristics of large datasets. Machine learning (ML) methods can analyse large datasets to extract novel insights on risk factors for obesity. This study predicted adults at risk of a ≥10% increase in index body mass index (BMI) within 12 months using ML and a large electronic medical records (EMR) database.

Materials and methods

ML algorithms were used with EMR from Optum's de-identified Market Clarity Data, a US database. Models included extreme gradient boosting (XGBoost), random forest, simple logistic regression (no feature selection procedure) and two

SUBMITTER: Choong C 

PROVIDER: S-EPMC12046438 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature