Predicting health, economy, and environmental indicators: a comparative machine learning approach

Abstract

This study aims to reveal the interaction between environmental sustainability and health policies by examining the relationships between economic growth, carbon emissions, and healthcare expenditures within the framework of machine learning methods. The primary motivation of the research is to evaluate the relationships among economic growth, health indicators, and environmental sustainability using data-driven methods. The analysis is based on a large-scale panel dataset covering 217 countries for the period 1960–2024, obtained from the World Bank World Development Indicators database. Advanced machine learning algorithms, including Extra Trees, Random Forest, Extreme Gradient Boosting, Histogram-based Gradient Boosting, and Light Gradient Boosting Machine, were implemented, and their prediction performances were compared. To improve prediction accuracy, a hybrid ensemble model was proposed by combining the highest-performing algorithms. For model interpretability, the relative importance levels of variables were analyzed using the Explainable Artificial Intelligence approach via Shapley Additive Explanations. The findings indicate that the proposed hybrid ensemble model provides higher prediction accuracy compared to individual algorithms. Results from the Shapley Additive Explanations analysis show that health expenditures, economic growth, and innovation-related indicators exhibited high predictive importance within the developed machine learning models. The findings highlight the predictive associations between environmental indicators, healthcare spending, and sustainable development outcomes. This study analyzes the complex relationships between economic growth, environmental sustainability, and health policies through a data-driven perspective. The findings provide additional evidence regarding the predictive associations among economic, environmental, and health-related indicators. The proposed framework demonstrates the usefulness of machine learning approaches for analyzing complex nonlinear patterns in large-scale country-year datasets.