Machine learning approach to identify country-specific drivers of global cancer outcomes.

1596 Background: Global disparities in access to cancer diagnostics and care drive substantial variation in cancer outcomes worldwide. Identifying actionable health system factors at the country level is critical to improving survival and closing global equity gaps. We applied explainable machine learning methods to explore the interrelations among these interrelated factors. Methods: We developed and validated a machine learning model using data from 185 countries, obtained from GLOBOCAN 2022, WHO, and the World Bank. Mortality-to-incidence ratios (MIRs) were predicted using repeated leave-one-country-out cross-validation. SHapley Additive exPlanations (SHAP) analysis decomposed each prediction into country-specific health system-feature attributions, revealing the principal drivers of cancer outcomes. Results: The model achieved robust performance (R²=0.852; RMSE=0.057; Pearson r=0.923, p<0.001). Globally, GDP per capita, radiotherapy center density, and the universal health coverage (UHC) index were the top contributors to MIRs. SHAP analysis revealed significant heterogeneity, identifying distinct priority drivers for each country. For example, radiotherapy infrastructure was most impactful in Turkey, UHC in Brazil and Ghana, and GDP per capita in Malaysia and China. Higher health spending as a percent of GDP was often paradoxically associated with higher MIR, underscoring the need not only for sufficient funding but also for strategic allocation. We developed a web tool that provides policymakers with country-specific SHAP estimates. Conclusions: Explainable machine learning translates global associations into actionable, country-specific insights. Access to radiotherapy, workforce development, and UHC expansion are consistently associated with improved cancer survival. These findings support resource prioritization in national cancer control planning and inform hypothesis generation for future causal studies. National-level analyses guide targeted investment in infrastructure and health coverage, potentially accelerating progress toward reducing global disparities in cancer mortality.