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Errors occurring during blood pressure monitoring01:25

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Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
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Developing a hypertension visualization risk prediction system utilizing machine learning and health check-up data.

Jinsong Du1,2, Xiao Chang1,2, Chunhong Ye1

  • 1School of Public Health and Clinical Medicine, Hangzhou Normal University, Hangzhou, 311121, China.

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This study developed a machine learning system to predict hypertension risk using health data. The system identifies key risk factors like age and triglycerides, aiding personalized prevention strategies.

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Area of Science:

  • Cardiovascular Disease Research
  • Health Informatics
  • Machine Learning Applications

Background:

  • Hypertension is a major risk factor for cardiovascular diseases, necessitating effective prevention and intervention strategies.
  • Current methods for hypertension management require convenient and reliable tools for personalized health tracking.
  • Machine learning offers potential for developing advanced risk prediction models.

Purpose of the Study:

  • To design a visualization risk prediction system for personalized hypertension management.
  • To utilize machine learning algorithms and SHAP for identifying hypertension risk factors.
  • To create an accessible web-based tool for patients and healthcare providers.

Main Methods:

  • Ten machine learning algorithms, including random forests, were employed.
  • 1617 anonymized health check datasets were used to train and validate hypertension risk prediction models.
  • Model performance was assessed using accuracy, F1-score, and ROC curve analysis.
  • The SHAP algorithm was integrated with the best-performing model for feature importance analysis.

Main Results:

  • The LightGBM model demonstrated superior predictive performance among the ten algorithms evaluated.
  • Key predictors for hypertension risk identified include age, alkaline phosphatase, and triglycerides.
  • A functional web-based visualization system was successfully developed.

Conclusions:

  • The developed system provides personalized hypertension risk probability and intervention focus.
  • This tool supports clinicians and patients in creating tailored prevention and intervention plans.
  • The research holds significant implications for clinical practice and public health in managing hypertension.