Identifying individuals at risk for weight gain using machine learning in electronic medical records from the United States.
Journal:
Diabetes, obesity & metabolism
PMID:
40069847
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.