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 and validation. Area under the receiver operating characteristic curves (AUC) were calculated to determine model accuracy at predicting high-risk individuals, defined by ≥ 10% total body weight gain within 2 y. Variable importance was measured via generalized analysis of variance procedures.

Results: Of the 24,183 patients (mean [SD] age, 32.0 [6.3] y; 55.1% females) in the study, 14.2% gained ≥10% total body weight. Area under the receiver operating characteristic curves varied from 0.557 (SVM) to 0.675 (gradient-boosted decision trees). Age, sex, and baseline body mass index were the most important predictors among the models except SVM and neural network.

Conclusions: Our machine learning models performed similarly and had modest accuracy for identifying young adults at risk of weight gain. Future models may need to incorporate behavioral and/or genetic information to enhance model accuracy.

Keywords: Adult; Machine learning; Obesity; Weight gain; Young.

Figure 1.

Cohort selection Flowchart of study cohort selection

Figure 2.

Area under receiver operating characteristic curves of the compared methods for prediction of weight gain risk Area under the receiver operator characteristic curves (AUCs) of the compared methods for prediction of ≥ 10% total body weight gain with 95% DeLong confidence intervals

Figure 3.

Variable importance plot All model variables are listed on y-axis. Model types are displayed on x-axis. Darker shading correlates with higher variable importance.