Multi-horizon machine learning for population-level hypertension risk stratification in the UK Biobank

Hypertension is a major contributor to cardiovascular morbidity and mortality, yet identifying individuals at risk before clinical diagnosis remains challenging. Here, we present a multi-horizon machine learning framework designed to model incident hypertension risk across multiple clinically meaningful time windows using data from 246 286 participants in the UK Biobank. The framework systematically compares predictive performance across five horizons under severe class imbalance, enabling analysis of how discrimination, precision, and risk drivers evolve as outcome prevalence changes over time.

Seven classification algorithms were evaluated, including logistic regression, random forest, naïve Bayes, and four boosting-based ensemble methods. To enhance interpretability, we integrate SHapley Additive exPlanations (SHAP) with generative topographic mapping (GTM), combining feature-level attribution with population-level visualization of model predictions. Ensemble boosting models consistently achieved the strongest performance, with average precision increasing from 0.04 for the ≤2-year horizon to 0.22 for the ≤10-year horizon, while ROC-AUC remained relatively stable (∼0.75–0.79). Together, this framework reveals consistent predictors of hypertension risk (including baseline blood pressure, age, body mass index, medication burden, and cardiometabolic multimorbidity) and illustrates how combinations of risk factors organize hypertension risk across time horizons.

Our results demonstrate how multi-horizon modelling and complementary explainability approaches can provide deeper insight into evolving disease risk patterns in large biomedical cohorts, supporting more interpretable and scalable strategies for population-level cardiovascular prevention. Such approaches may enable earlier identification of high-risk individuals and inform targeted screening and preventive interventions in routine care settings.