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

doi:10.3389/fpubh.2021.619429

	Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method
	Zhao, Huanhuan 1,2,3; Zhang, Xiaoyu 1,2; Xu, Yang 1; Gao, Lisheng 1; Ma, Zuchang 1; Sun, Yining 1; Wang, Weimin 4
刊名	FRONTIERS IN PUBLIC HEALTH
	2021-09-24
卷号	9
关键词	hypertension risk prediction machine learning method easy-to-collect lifestyle
DOI	10.3389/fpubh.2021.619429
通讯作者	Ma, Zuchang(zcma@iim.ac.cn)
英文摘要	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.
资助项目	major special project of Anhui Science and Technology Department[18030801133] ; Science and Technology Service Network Initiative[KFJ-STS-ZDTP-079]
WOS关键词	BLOOD-PRESSURE ; PREVALENCE ; OBESITY ; AGE
WOS研究方向	Public, Environmental & Occupational Health
语种	英语
出版者	FRONTIERS MEDIA SA
WOS记录号	WOS:000704546000001
资助机构	major special project of Anhui Science and Technology Department ; Science and Technology Service Network Initiative
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/124820]
专题	中国科学院合肥物质科学研究院
通讯作者	Ma, Zuchang
作者单位	1.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei, Peoples R China 2.Univ Sci & Technol China, Sci Isl Branch, Grad Sch, Hefei, Peoples R China 3.Chuzhou Univ, Sch Comp & Informat Engn, Chuzhou, Peoples R China 4.Chinese Peoples Liberat Army PLA Gen Hosp, Inst Hlth Management, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Huanhuan,Zhang, Xiaoyu,Xu, Yang,et al. Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method[J]. FRONTIERS IN PUBLIC HEALTH,2021,9.
APA	Zhao, Huanhuan.,Zhang, Xiaoyu.,Xu, Yang.,Gao, Lisheng.,Ma, Zuchang.,...&Wang, Weimin.(2021).Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method.FRONTIERS IN PUBLIC HEALTH,9.
MLA	Zhao, Huanhuan,et al."Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method".FRONTIERS IN PUBLIC HEALTH 9(2021).