Prediction of Hypertension Risk in Middle-Aged and Elderly Populations Using Machine Learning

Abstract

This paper presents a machine learning prediction framework of “dual feature selection-multi-model comparison-cross-population validation” to address insufficient generalizability and lack of external validation in current hypertension risk prediction models for middle-aged and elderly populations. Based on CHARLS cohort data and independent NHANES validation data, we integrated multi-dimensional variables, such as demographic, anthropometric, blood pressure, lifestyle, and chronic disease variables, and performed joint feature selection using univariate Logistic regression and Lasso regression, identifying 13 core variables. Three models—Logistic Regression, Random Forest, and XGBoost—were constructed. Performance was evaluated using ROC curves and AUC, and robustness and interpretability were verified using 5-fold cross-validation and SHAP value analysis. Results showed that the XGBoost model exhibited the best overall performance, with an internal AUC of 0.942 and an external AUC of 0.830, a difference of only 0.112, indicating no significant over-fitting. Age, BMI, systolic blood pressure, and diastolic blood pressure were identified as the core risk factors. The XGBoost model constructed in this study combines optimal discriminative power with cross-population generalization stability, providing a scientific reference for early screening and individualized prevention of hypertension in middle-aged and elderly populations. Furthermore, the supplementary analysis model based on non-blood pressure indicators also demonstrated good early warning efficacy, forming a hierarchical complement to the main model and providing a non-invasive technical solution for community-based initial screening of hypertension in middle-aged and elderly populations.