Determinants of blood pressure control in hypertensive individuals using histogram-based gradient boosting: findings from 1114 male workers in South Korea

Abstract

Hypertension is a significant public health concern, particularly among workers, due to its association with increased risk of cardiovascular and cerebrovascular diseases. This study aimed to identify key factors influencing blood pressure control in hypertensive male workers aged 40 and above using the Histogram-based Gradient Boosting (HGB) algorithm. Data were drawn from the 2017–2020 Korean National Health and Nutrition Examination Survey (KNHANES), including 1114 male participants who reported being diagnosed with hypertension by a physician. The HGB model was compared with five other machine learning models: Random Forest, XGBoost, LightGBM, CatBoost and AdaBoost. The HGB model demonstrated superior performance with an accuracy of 82.3%, precision of 80.5%, recall of 78.9% and F1-score of 79.7%. Feature importance analysis revealed that age, Body Mass Index (BMI) and physical activity were the most significant factors influencing blood pressure control. Other notable factors included sodium intake, stress levels and medication adherence. The study’s findings underscore the importance of targeted interventions focusing on these key factors to improve hypertension management strategies. By employing advanced machine learning techniques, this research provides valuable insights into the determinants of blood pressure control, offering a foundation for developing effective strategies to reduce hypertension-related complications and mortality among Korean male workers.

Keywords

• hypertension
• blood pressure control
• machine learning
• gradient boosting
• male worker