[Screening biomarkers for hypertensive heart disease: Analysis based on data from 7 medical institutions].

Abstract

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.

• Authors: 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
• Journal: Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology (Zhongguo Ying Yong Sheng Li Xue Za Zhi) Vol. 37 Issue 2 Pg. 142-146 (Mar 2021) ISSN: 1000-6834 [Print] China
• PMID: 34672151 (Publication Type: Journal Article)

Chemical References

• Biomarkers

Topics

• Biomarkers
• Heart Diseases
• Humans
• Machine Learning