Machine Learning Prediction of Household Out-of-Pocket Health Expenditure in Ghana: A Comparative Model Analysis

Abstract Background Financial protection is a central goal of universal health coverage, yet many households in Ghana continue to face high out-of-pocket (OOP) health expenditures despite government and donor financing. Understanding how these financing sources influence household financial risk is important for strengthening health system sustainability and equity. Methods This study used a longitudinal ecological design with secondary data from the World Health Organization Global Health Expenditure Database for Ghana from 2000 to 2023. Descriptive statistics was conducted to examine trends. Multivariate time-series regression was applied to estimate the linear effects of government health expenditure and external donor financing on household OOP expenditure. A comparative model analysis was conducted to evaluate the predictive performance of multiple machine learning algorithms in estimating household out-of-pocket health expenditure. Results Household OOP expenditure averaged 33.1% of total health spending. Both government and external financing significantly reduced OOP expenditure, with government spending showing the strongest effect. The Decision Tree model explained 87.8% of the variation in household OOP expenditure. Conclusion Strengthening domestic public health investment, alongside strategic donor support, is essential for reducing household financial burden and advancing universal health coverage in Ghana.