Explaining Disparities in Heat-related Hospital Admissions Using Explainable AI: A Comparative Analysis across Global Income Levels
Keywords:
Climate change, Explainable AI, Global health, Heat-related illness, XGBoostAbstract
Heat-related illness has become a serious public health issue all over the world because of the increase in heat waves and the rise in temperature globally. It is very well established that heat can cause a wide variety of health-related problems, such as heart problems, kidney problems, respiratory problems and metabolic problems; however, the differences in heat-related hospital admissions by country income have not been studied in detail. This study aims to investigate the global predictors of heat-related hospitalizations and how those predictors differ between high and low/middle-income countries (LMIC) using explainable artificial intelligence (XAI) to improve understanding of the results. The analysis uses the global climate-health impact tracker dataset, which contains 14,100 records from 141 countries over the time period of 2015–2025. An XGBoost regression model was built using 23 predictors that demonstrated acceptable performance, R² = 0.8245; RMSE = 0.5564; MAE = 0.3458, further validated through cross-validation and comparison with baseline models. The model interpretability was performed using permutation importance and SHAP values. The results show that globally, temperature is the most important predictor of heat-related hospital admissions, and heat wave duration is the second most important predictor of heat-related hospital admissions. Additionally, LMICs have a higher level of vulnerability to heat-related hospital admissions due to greater exposure to heat and having limited capacity to adapt to the heat, and high-income countries have more complex interactions between environmental and healthcare-related factors. The findings of this study support the need for more tailored public health strategies and climate adaptation policies on a country-by-country basis.
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