Spatial modeling and machine learning-based assessment of regional stroke risk and predictors in Ghana: a cross-sectional study

Abstract Aim Stroke remains a leading global cause of death and disability, and its incidence is rising in Ghana, posing a significant public health concern. However, comprehensive data on its spatial distribution across Ghana’s 16 regions are limited. This study aimed to assess the spatial distribution of stroke risk and to identify high-risk regions and associated risk factors. Subject and methods Using nonparametric ensemble machine learning models—random forest and gradient boosting—the study performed variable selection and predicted stroke risk. Key predictors identified were incorporated into a Bayesian spatial model (BYM2) to estimate region-specific relative risk (RR). Posterior estimates were mapped to visualize spatial trends, and interpretability tools such as partial dependence plots and SHAP (SHapley Additive exPlanations) values were used to analyze covariate effects. Results Results showed a modest overall increase in stroke risk (3%), with notable regional variation. The Volta and Central regions exhibited the highest risk (relative risk [RR] = 3.0–3.5 and 2.5–3.0), while the Savannah and Northern regions had the lowest (RR = 0.0–1.0). Gradient boosting outperformed random forest (75% vs. 13% accuracy), identifying gross national income (GNI) and diabetes prevalence as top predictors. Higher GNI was linked to reduced stroke risk (RR = 0.95), whereas increased diabetes prevalence was associated with higher risk (RR = 1.18). Stroke risk decreased sharply at a GNI threshold of 26% and rose steadily with diabetes prevalence. Regions with high GNI and low diabetes prevalence had lower stroke counts. Conclusion The study highlights significant regional disparities and key predictors of stroke risk in Ghana, offering valuable insights for targeted public health strategies and equitable resource allocation.