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Objective: To screen the influencing factors of hypertensive heart disease (HHD), establish the predictive model of HHD, and provide early warning for the occurrence of HHD. Methods: Select the patients diagnosed as hypertensive heart disease or hypertensionfrom January 1, 2016 to December 31, 2019, in the medical data science academy of a medical school. Influencing factors were screened through single factor and multi-factor analysis, and R software was used to construct the logistics model, random forest (RF) model and extreme gradient boosting (XGBoost) model. Results: Univariate analysis screened 60 difference indicators, and multifactor analysis screened 18 difference indicators (P<0.05). The area under the curve (AUC) of Logistics model, RF model and XGBoost model are 0.979, 0.983 and 0.990, respectively. Conclusion: The results of the three HHD prediction models established in this paper are stable, and the XGBoost prediction model has a good diagnostic effect on the occurrence of HHD.


Xue-Mei Zhang, Xiao-Gang Zhong, Jun Gong, Jun Tian, Yi Zhang, Ying-Zhe Chen, Jing Cui, Zeng-Zi Wang, Shu-Qiong Ran, Tian-Yu Xiang, You-Hong Xie, Xing-Guo Sun. Screening biomarkers for hypertensive heart disease: Analysis based on data from 7 medical institutions]. Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology. 2021 Mar;37(2):142-146

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PMID: 34672151

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