Yanchen Cai
ABSTRACT
With the change of national lifestyle and the aggravation of population aging, the incidence of hypertension in China is increasing year by year, which has become a major public health problem. Based on this, this study selected data from a survey conducted by the China Health and Nutrition Survey (CHNS), and carried out univariate and multivariate analyses of the risk factors of hypertension, and 10 variables of high correlation were extracted to construct hypertensive diseases risk prediction models using Random Forest, XGBoost and LightGBM algorithms. Overall, combining the accuracy, precision, recall and AUC value of the models, the Random Forest prediction model is the most effective among all the models with 91.21% accuracy, 88.89% precision, 93.31% recall and 95.27% AUC value.The prediction model has good credibility and accuracy, which provides a scientific basis for the screening of people at high risk of hypertension and reduces the risk of hypertension in a certain extent.
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Index Terms
- Integrated Learning Based Risk Prediction Study for Hypertensive Diseases
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