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. 2021 Sep 24:9:619429.
doi: 10.3389/fpubh.2021.619429. eCollection 2021.

Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method

Huanhuan Zhao  1   2   3 , Xiaoyu Zhang  1   2 , Yang Xu  1 , Lisheng Gao  1 , Zuchang Ma  1 , Yining Sun  1 , Weimin Wang  4
Affiliations

Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method

Huanhuan Zhao et al. Front Public Health. .

Abstract

Hypertension is a widespread chronic disease. Risk prediction of hypertension is an intervention that contributes to the early prevention and management of hypertension. The implementation of such intervention requires an effective and easy-to-implement hypertension risk prediction model. This study evaluated and compared the performance of four machine learning algorithms on predicting the risk of hypertension based on easy-to-collect risk factors. A dataset of 29,700 samples collected through a physical examination was used for model training and testing. Firstly, we identified easy-to-collect risk factors of hypertension, through univariate logistic regression analysis. Then, based on the selected features, 10-fold cross-validation was utilized to optimize four models, random forest (RF), CatBoost, MLP neural network and logistic regression (LR), to find the best hyper-parameters on the training set. Finally, the performance of models was evaluated by AUC, accuracy, sensitivity and specificity on the test set. The experimental results showed that the RF model outperformed the other three models, and achieved an AUC of 0.92, an accuracy of 0.82, a sensitivity of 0.83 and a specificity of 0.81. In addition, Body Mass Index (BMI), age, family history and waist circumference (WC) are the four primary risk factors of hypertension. These findings reveal that it is feasible to use machine learning algorithms, especially RF, to predict hypertension risk without clinical or genetic data. The technique can provide a non-invasive and economical way for the prevention and management of hypertension in a large population.

Keywords: easy-to-collect; hypertension; lifestyle; machine learning method; risk prediction.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
ROC curve for the test set of each model.
Figure 2
Figure 2
Confusion matrix for the test set of each model. (A) RF model, (B) CatBoost model, (C) MLP neural network model, (D) LR model.
Figure 3
Figure 3
Feature importance ranking for the RF model.
See this image and copyright information in PMC

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