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Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health
Manhui Zhang1, Xian Xia1, Qiqi Wang2
1Department of Disease Control and Prevention, The Seventh Medical Center of Chinese PLA General Hospital.
Insights
Machine learning, including the AMFormer model, can predict new onset hypertension risk. Key factors like age and BMI are crucial for identifying individuals at high risk.
Area of Science:
- Cardiovascular Disease Research
- Machine Learning in Healthcare
- Epidemiological Studies
Background:
- Hypertension is a major risk factor for cardiovascular diseases, impacting heart, brain, and kidney health.
- Predicting new onset hypertension is crucial for early intervention and disease management.
Purpose of the Study:
- To predict the risk of new onset hypertension using machine learning algorithms.
- To identify key patient characteristics associated with developing hypertension.
Main Methods:
- Analysis of the 2011 China Health and Nutrition Survey cohort (n=4,982) with follow-up data until 2015.
- Evaluation of six machine learning models: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, TabNet, and AMFormer.
- Feature selection using SHAP values for identifying hypertension risk factors.
Main Results:
- 1,017 participants developed hypertension during the 4-year follow-up.
- The AMFormer model, using 29 features, achieved the highest AUC (0.802), MCC (0.417), and F1 (0.503) scores.
- Significant predictors included age, province, waist circumference, urban/rural status, education, employment, weight, WHR, and BMI.
Conclusions:
- The AMFormer model demonstrated superior performance in predicting new onset hypertension compared to other tested algorithms.
- This study highlights the potential of machine learning for enhancing disease prediction and identifying risk factors.
- Key demographic and anthropometric features are critical for predicting hypertension development.
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.
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