A Machine Lear ning Model for Pr edicting Obesity Risk in Patients with Diabetes Mellitus: Analysis of NHANES 2007-2018

Sijie Yang^1*Ruiqing Mo²Xingyu Chen²Wenqiang Wang^2*

• ¹Department of Plastic and Reconstructive Surgery, The People’s Hospital of Guangxi Zhuang Autonomous Region & Research Center of Medical Sciences, Guangxi Academy of Medical Sciences, Nanning, Guangxi, China
• ²Department of Bone and Joint Surgery, (Guangxi Diabetic Foot Salvage Engineering Research Center), The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China

Background: Obesity is a prevalent and clinically significant complication among individuals with diabetes mellitus (DM), contributing to increased cardiovascular risk, metabolic burden, and reduced quality of life. Despite its high prevalence, the risk factors for obesity within this population remain incompletely understood. With the growing availability of large-scale health datasets and advancements in machine learning, there is an opportunity to improve risk stratification. This study aimed to identify key predictors of obesity and develop a machine learning-based predictive model for patients with T2DM using data from the National Health and Nutrition Examination Survey (NHANES). Methods: Data from adults with diabetes were extracted from the NHANES 2007-2018 cycles. Participants were categorized into obese and non-obese groups based on BMI. Least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was used to select relevant features. Subsequently, nine machine learning algorithms-including logistic regression, random forest (RF), radial support vector machine (RSVM), k-nearest neighbors (KNN), XGBoost, LightGBM, decision tree (DT), elastic net regression (ENet), and multilayer perceptron (MLP)-were employed to construct predictive models. Model performance was evaluated based on area under the ROC curve (AUC), calibration curves, Brier score, and decision curve analysis (DCA). The best-performing model was visualized using a nomogram to enhance clinical applicability. Results: A total of 3,794 participants with type 2 diabetes were included in the analysis, of whom 57.0% were classified as obese. LASSO regression identified 19 key variables associated with obesity. Among the nine machine learning models evaluated, the logistic regression model demonstrated the best overall performance, with the lowest Brier score. It also showed good discrimination (AUC = 0.751 in the training set and 0.781 in the test set), favorable calibration, and consistent clinical utility based on decision curve analysis (DCA). A nomogram was constructed based on the logistic regression model to facilitate individualized risk prediction, with total points corresponding to predicted probabilities of obesity. Conclusion: Obesity remains highly prevalent among individuals with type 2 diabetes. Our findings highlight key clinical features associated with obesity risk and provide a practical tool to aid in early identification and individualized management of high-risk patients.