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

Frontiers in Public Health

Weimin Wang¹  Zuchang Ma²  Yining Sun²  Yang Xu²  Lisheng Gao²  Xiaoyu Zhang³  Huanhuan Zhao⁴ 

[1] Institute of Health Management, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China;Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China;Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China;Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, China;Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China;Science Island Branch of Graduate School, University of Science and Technology of China, Hefei, China;School of Computer and Information Engineering, Chuzhou University, Chuzhou, China;

关键词: hypertension;    risk prediction;    machine learning method;    easy-to-collect;    lifestyle;   

DOI  :  10.3389/fpubh.2021.619429

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY