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Updated: Oct 16, 2025

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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[Screening biomarkers for hypertensive heart disease: Analysis based on data from 7 medical institutions]

Xue-Mei Zhang1, Xiao-Gang Zhong1,2, Jun Gong2

  • 1Department of Medical and Nursing, The Affiliated Rehabilitation Hospital of Chongqing Medical University, Chongqing 400050.

Zhongguo Ying Yong Sheng Li Xue Za Zhi = Zhongguo Yingyong Shenglixue Zazhi = Chinese Journal of Applied Physiology
|October 21, 2021
PubMed

Insights

Researchers identified key factors for hypertensive heart disease (HHD) and developed predictive models. The XGBoost model demonstrated the best diagnostic performance for early HHD warning.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Data Science

Background:

  • Hypertensive heart disease (HHD) poses a significant health risk.
  • Early detection and prediction of HHD are crucial for timely intervention.
  • Identifying influencing factors can aid in risk stratification.

Purpose of the Study:

  • To identify factors influencing the development of HHD.
  • To establish robust predictive models for HHD.
  • To enable early warning systems for HHD occurrence.

Main Methods:

  • Patient data from 2016-2019 was analyzed.
  • Single-factor and multi-factor analyses were performed to screen indicators.
  • Logistics, Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models were constructed using R software.

Main Results:

  • 18 significant influencing factors were identified through multifactor analysis (P<0.05).
  • The Area Under the Curve (AUC) values for the models were: Logistics (0.979), RF (0.983), and XGBoost (0.990).
  • The XGBoost model exhibited the highest predictive accuracy.

Conclusions:

  • The developed HHD prediction models are stable and reliable.
  • The XGBoost model shows excellent diagnostic capability for HHD.
  • These models can contribute to early HHD risk assessment and prevention.

Keywords:
XGBoostbiomarkershypertensive heart diseasemachine learningrandom forest

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