Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey.

Manhui ZHANG; Xian XIA; Qiqi WANG; Yue PAN; Guanyi ZHANG; Zhigang WANG

Return

Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey.

Author: Manhui ZHANG ¹ ; Xian XIA ¹ ; Qiqi WANG ² ; Yue PAN ¹ ; Guanyi ZHANG ³ ; Zhigang WANG ¹
Author Information

1. Department of Disease Control and Prevention, The Seventh Medical Center of Chinese PLA General Hospital.
2. Office of Epidemiology (Technical Guidance Office for Patriotic Health Work), Chinese Center for Disease Control and Prevention.
3. Department of Neurology, Beijing Tiantan Hospital, Capital Medical University.
Publication Type:Journal Article
Keywords: CHNS; Machine learning algorithms; New onset hypertension; Prediction
MeSH: Humans; China/epidemiology*; Hypertension/diagnosis*; Machine Learning; Male; Female; Middle Aged; Adult; Nutrition Surveys; Algorithms; Aged; Risk Factors
From:Environmental Health and Preventive Medicine 2025;30():3-3
CountryJapan
Language:English
Abstract: BACKGROUND:Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as the heart, brain, and kidneys. Our goal is to predict the risk of new onset hypertension using machine learning algorithms and identify the characteristics of patients with new onset hypertension.
METHODS:We analyzed data from the 2011 China Health and Nutrition Survey cohort of individuals who were not hypertensive at baseline and had follow-up results available for prediction by 2015. We tested and evaluated the performance of four traditional machine learning algorithms commonly used in epidemiological studies: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, and two deep learning algorithms: TabNet and AMFormer model. We modeled using 16 and 29 features, respectively. SHAP values were applied to select key features associated with new onset hypertension.
RESULTS:A total of 4,982 participants were included in the analysis, of whom 1,017 developed hypertension during the 4-year follow-up. Among the 16-feature models, Logistic Regression had the highest AUC of 0.784(0.775∼0.806). In the 29-feature prediction models, AMFormer performed the best with an AUC of 0.802(0.795∼0.820), and also scored the highest in MCC (0.417, 95%CI: 0.400∼0.434) and F1 (0.503, 95%CI: 0.484∼0.505) metrics, demonstrating superior overall performance compared to the other models. Additionally, key features selected based on the AMFormer, such as age, province, waist circumference, urban or rural location, education level, employment status, weight, WHR, and BMI, played significant roles.
CONCLUSION:We used the AMFormer model for the first time in predicting new onset hypertension and achieved the best results among the six algorithms tested. Key features associated with new onset hypertension can be determined through this algorithm. The practice of machine learning algorithms can further enhance the predictive efficacy of diseases and identify risk factors for diseases.