Identifying Young Adults at High Risk for Weight Gain Using Machine Learning.

ABSTRACT:

Introduction

Weight gain among young adults continues to increase. Identifying adults at high risk for weight gain and intervening before they gain weight could have a major public health impact. Our objective was to develop and test electronic health record-based machine learning models to predict weight gain in young adults with overweight/class 1 obesity.

Methods

Seven machine learning models were assessed, including three regression models, random forest, single-layer neural network, gradient-boosted decision trees, and support vector machine (SVM) models. Four categories of predictors were included: 1) demographics; 2) obesity-related health conditions; 3) laboratory data and vital signs; and 4) neighborhood-level variables. The cohort was split 60:40 for model training

SUBMITTER: Murtha JA 

PROVIDER: S-EPMC10524852 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature