Construction，Validation，and Explainability Study of a Hypertension Risk Identification Model Based on Machine Learning

Wenjun LI; Lang FANG; Yanmei JI; Meirong YANG; Ni MENG; Xinyi LIU; Xingfang JIN

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Wenjun LI, Lang FANG, Yanmei JI, Meirong YANG, Ni MENG, Xinyi LIU, Xingfang JIN. Construction，Validation，and Explainability Study of a Hypertension Risk Identification Model Based on Machine Learning[J]. Journal of Kunming Medical University.

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Construction，Validation，and Explainability Study of a Hypertension Risk Identification Model Based on Machine Learning

1.
ICU of Cardiovascular and Thoracic Surgery
2.
Department of Central Laboratory，Yan'an Affiliated Hospital of Kunming Medical University，Kunming Yunnan 650051，China

Received Date: 2025-11-17

Abstract

Abstract

Objective To develop and validate a machine learning （ML）-based model for hypertension risk identification, and to explore the potential of the optimal model in clinical application. Methods A chronic disease cross-sectional survey was conducted in Yunnan from September 2019 to December 2023, with permanent residents aged 35–75 years in multiethnic communities of Yunnan Province selected as the study population using stratified cluster sampling. The survey included 1, 435 participants; after data preprocessing, 848 subjects were included. During the survey, data on sociodemographic characteristics, smoking and alcohol consumption history, chronic diseases, and medication use were collected. The dataset was randomly split into a training set and a test set at an 8:2 ratio. Six ML models were developed, including logistic regression （LR）, decision tree （DT）, random forest （RF）, eXtreme gradient boosting （XGBoost）, categorical boosting （CatBoost）, and multilayer perceptron （MLP）. Hyperparameters were optimized by grid search and five-fold cross-validation. Model performance was comprehensively evaluated using accuracy, sensitivity, specificity, F1 score, area under the receiver operating characteristic curve （AUC and 95%CI）, decision curve analysis （DCA）, and calibration curves. The optimal model was interpreted using SHapley Additive exPlanations （SHAP）, and a small-sample preliminary exploratory generalization validation was performed. Results A total of 848 participants were included. The models incorporated features such as sociodemographic variables, physical examination findings, and blood biochemical indicators. On the test set, the RF model performed best, with an accuracy of 0.926, specificity of 0.943, F1-score of 0.923, and AUC of 0.972 （95%CI: 0.950～0.993, P < 0.05）. DCA showed that the RF model provided the highest clinical benefit across a broad range of threshold probabilities. SHAP analysis revealed that age, body weight, blood lipids, blood glucose, and uric acid were key features for identifying hypertension （P<0.05）. In the 20 cases used for exploratory generalization validation, the RF model achieved 100% identification accuracy. Multivariable logistic regression analysis confirmed that a history of medication use （OR = 16.837, 95%CI: 11.344～24.990）, systolic blood pressure （OR = 1.032, 95%CI: 1.019～1.044）, and uric acid （OR = 1.005, 95%CI: 1.003～1.008） were independent risk factors for hypertension （P < 0.05）. Conclusion The machine learning model constructed based on random forest algorithm shows good discriminative performance in internal testing and may provide reference for the auxiliary clinical screening of hypertension and multidimensional feature quantification, but its clinical generalizability still needs to be confirmed by a larger prospective cohort study.
- Hypertension,
- Machine learning,
- Random forest,
- Risk identification,
- Clinical decision support

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References(32)

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