Predicting Global Health Expenditures Using Machine Learning and Regularized Regression Methods

Authors : Hakan Öztürk, Elvan Hayat

Pages : 462-476

Doi:10.47097/piar.1792425

View : 59 | Download : 122

Publication Date : 2025-12-29

Article Type : Research Paper

Abstract :Health expenditures are crucial for countries’ economic sustainability and the effectiveness of health policies. Accurately modeling these expenditures is complex and requires methods beyond classical regression. This study aimed to estimate per capita health expenditures using machine learning and regularized regression approaches based on 2022 World Bank data from 190 countries. Missing values were imputed using the Multiple Imputation by Chained Equations (MICE) method. The dependent variable was per capita health expenditure, while independent variables included socioeconomic and demographic indicators. Six models—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Elastic Net, Lasso, and Ridge—were compared using RMSE, MAE, and R² metrics. SVR achieved the best performance (RMSE = 463 ± 13.3, R² = 0.940 ± 0.003). XGBoost yielded the lowest MAE (262 ± 15.5) with high accuracy (R² = 0.923 ± 0.007). GDP per capita was the most important predictor, followed by the proportion of elderly population, life expectancy, and urbanization rate. SVR and XGBoost models demonstrated high predictive power, highlighting their potential as decision-support tools for forecasting health expenditures.
Keywords : Sağlık Harcaması, Makine Öğrenmesi, XGBoost, Destek Vektör Regresyonu, Rastgele Ormanlar

ORIGINAL ARTICLE URL