Makine Öğrenmesi ve Düzenlileştirilmiş Regresyon Yöntemleri ile Küresel Sağlık Harcamalarının Tahmini

Year 2025, Volume: 12 Issue: 2, 462 - 476, 29.12.2025

Hakan Öztürk , Elvan Hayat

https://doi.org/10.47097/piar.1792425

Abstract

Sağlık harcamaları, ülkelerin ekonomik sürdürülebilirliği ve sağlık politikalarının etkinliği açısından kritik öneme sahiptir. Bu harcamaların doğru biçimde modellenmesi karmaşık bir süreçtir ve klasik regresyon yöntemlerinin ötesinde yaklaşımlar gerektirir. Bu çalışma, 190 ülkenin 2022 yılına ait Dünya Bankası verileri kullanılarak kişi başına sağlık harcamalarını makine öğrenmesi ve düzenlileştirilmiş regresyon yöntemleriyle tahmin etmeyi amaçlamıştır.
Veri bütünlüğünü sağlamak için eksik değerler Zincirleme Denklemlerle Çoklu Atama (MICE) yöntemiyle tamamlanmıştır. Bağımlı değişken kişi başına sağlık harcaması olup, bağımsız değişkenler sosyoekonomik ve demografik göstergeleri içermektedir. Altı model—Destek Vektör Regresyonu (SVR), Rastgele Ormanlar (RF), Aşırı Gradyan Artırma (XGBoost), Elastic Net, Lasso ve Ridge regresyonu—RMSE, MAE ve R² ölçütleri kullanılarak karşılaştırılmıştır. En iyi performans SVR modeliyle elde edilmiştir (RMSE = 463 ± 13.3, R² = 0.940 ± 0.003). XGBoost modeli en düşük MAE değerine (262 ± 15.5) ve yüksek doğruluk oranına (R² = 0.923 ± 0.007) ulaşmıştır. Kişi başına düşen GSYİH en güçlü yordayıcı olurken, yaşlı nüfus oranı, yaşam beklentisi ve kentleşme oranı ikincil katkılar sağlamıştır. SVR ve XGBoost modelleri yüksek tahmin gücü sergileyerek sağlık harcamalarının öngörülmesinde politika yapıcılar için değerli karar destek araçları olarak öne çıkmaktadır.

Keywords

Sağlık Harcaması , Makine Öğrenmesi , XGBoost , Destek Vektör Regresyonu , Rastgele Ormanlar

Ethical Statement

Çalışmalarda gerçekleştirilen tüm prosedürler, benzer kurumsal ve/veya ulusal araştırma komitelerinin etik standartlarına uygundur.

Supporting Institution

Herhangi bir kurum veya kuruluştan destek alınmamaktadır.

Thanks

.

Predicting Global Health Expenditures Using Machine Learning and Regularized Regression Methods

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

Health Expenditure , Machine Learning , XGBoost , Support Vector Regression , Random Forest

Ethical Statement

All procedures performed in studies comply with the ethical standards of comparable institutional and/or national research committees.

Supporting Institution

No support is taken from any institution or organization.

Thanks

.

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