Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method
文献类型：期刊论文
| 作者 | Zhao, Huanhuan1,2,3; Zhang, Xiaoyu1,2; Xu, Yang1 ; Gao, Lisheng1; Ma, Zuchang1; Sun, Yining1; Wang, Weimin4
|
| 刊名 | FRONTIERS IN PUBLIC HEALTH
 |
| 出版日期 | 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. |
| WOS关键词 | BLOOD-PRESSURE ; PREVALENCE ; OBESITY ; AGE |
| 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研究方向 | Public, Environmental & Occupational Health |
| 语种 | 英语 |
| WOS记录号 | WOS:000704546000001 |
| 出版者 | FRONTIERS MEDIA SA |
| 资助机构 | major special project of Anhui Science and Technology Department ; Science and Technology Service Network Initiative |
| 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). |
入库方式： OAI收割
来源：合肥物质科学研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。