[Screening biomarkers for hypertensive heart disease: Analysis based on data from 7 medical institutions].
Journal:
Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology
Published Date:
Mar 1, 2021
Abstract
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. 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. 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. 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.