Machine learning identification of influencing factors of global Nation-Level hypertension prevalence

Hypertension remains a critical global public health challenge, with its complex etiology poorly captured by traditional linear models, especially regarding macro-level structural and gender-specific drivers. To address this, we employed an interpretable machine learning framework, combining XGBoost with SHAP and bootstrap resampling. This approach analyzed a global, nation-level panel dataset from 190 countries (1990-2019) across four macro dimensions: natural geography, behavior, socioeconomic status, and healthcare. The XGBoost model achieved excellent predictive performance. SHAP analysis identified mean annual precipitation (PRC), hospital beds (HOS), obesity prevalence (OB), and access to safely managed drinking water as the dominant macro-determinants of global hypertension prevalence. Critically, factor influence showed profound gender heterogeneity: HOS was the most impactful predictor for males, while OB and specific socioeconomic indicators were key drivers for females. Our findings offer a transparent and actionable perspective for policymakers, underscoring the necessity of integrating macro-environmental and gender-stratified insights to formulate precise and equitable public health interventions and resource allocation strategies globally.