Predicting Health Spending in Turkey Using the GPR, SVR, and DT Models

Abstract

Rising healthcare costs for countries and the long-term maintainability of this situation are at the center of the political agenda. The steady increase in health spending puts pressure on government budgets, healthcare, and personal patient financing. Policymakers would like to plan reforms to reduce these costs to adapt to problems that may arise. This has led planners to decision support systems and forecasting models. In this paper, three machine learnings algoritms, namely Support Vector Regression (SVR), Decision Tree Regression (DT), and Gaussian Process Regression (GPR) are employed to design a forecasting model for Health Spendings (HS) of Turkey considering various determinants. Gross domestic product per capita, urban population rate, unemployment rate, population ages 65 and above, the life expectancy, the physicians’ rate, and the total number of hospital beds are used as inputs. The data set consists of 30 years between 1990-2019, which splits as training and test sets. Developed models were compared considering performance metrics, and the most accurate model was identified. The coefficient of determinations (R2) for SVR, GPR, and DT models are 0.9929, 0.9989, and 0.9611 in the training phase, 0.9536, 0.8944, and 0.1166 in the testing stage, respectively. Therefore, the SVR model has accurate prediction results with the highest R2 and the least root mean square error values in the testing phase. The study showed that the proposed SVR model reduced RMSE value by 32.02% and 39.66% compared to the GPR and DT models, respectively. Consequently, the Health Spendings of Turkey can be predicted by employing SVR with high accuracy.

Keywords

• Health Spending
• Gaussian Process Regression
• Decision Tree
• Support Vector Regression

Türkiye Sağlık Harcamalarının GPR, SVR ve DT Modelleri ile Tahmini

Abstract

Ülkeler için artan sağlık maliyetleri ve bu durumun uzun vadeli sürdürülebilirliği siyasi gündemin merkezinde yer almaktadır. Sağlık harcamalarındaki sürekli artış, hükümet bütçeleri, sağlık hizmetleri ve kişisel hasta finansmanı üzerinde baskı oluşturmaktadır. Politika yapıcılar, ortaya çıkabilecek sorunlara uyum sağlamak ve bu maliyetleri düşürmek için reformlar planlamak isterler. Bu durum, planlayıcıları karar destek sistemlerine ve tahmin modellerine yönlendirmiştir. Bu çalışmada, Türkiye’nin Sağlık Harcaması (HS) için çeşitli belirleyicileri dikkate alan bir tahmin modeli tasarlamak amacıyla Destek Vektör Regresyonu (SVR), Regresyon Ağacı (DT) ve Gauss Süreç Regresyonu (GPR) olmak üzere üç makine öğrenme algoritması kullanılmıştır. Kişi başına gayri safi yurtiçi hasıla, kentsel nüfus oranı, işsizlik oranı, 65 yaş ve üstü nüfus, ortalama yaşam süresi, hekim oranı ve toplam hastane yatak sayısı girdi değişkenleri olarak belirlenmiştir. Veri seti eğitim ve test verisi olarak ayrılmış ve 1990-2019 yılları arası 30 yılı kapsamaktadır. Geliştirilen modeller performans ölçütleri dikkate alınarak karşılaştırılmış ve en iyi model belirlenmiştir. SVR, GPR ve DT modelleri için belirleme katsayısı (R2) eğitim aşamasında sırasıyla 0.9929, 0.9989 ve 0.9611, test aşamasında sırasıyla 0.9536, 0.8944 ve 0.1166’dır. Ayrıca, SVR modeli, test aşamasında en yüksek R2 ve en düşük kök ortalama kare hatası değerleri ile en iyi tahmin sonuçlarına sahiptir. Çalışma, önerilen SVR modelinin RMSE değerini diğer GPR ve DT modellerine kıyasla sırasıyla % 32.02 ve % 39.66 azalttığını göstermiştir. Sonuç olarak, Türkiye’nin sağlık harcamaları SVR modeli kullanılarak yüksek doğrulukta tahmin edilebilir.

Keywords

• Sağlık Harcaması
• Gauss Süreç Regresyonu
• Regresyon Ağacı
• Destek Vektör Regresyonu

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