Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models
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Tarih
2021
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Rising healthcare costs for countries and the long-term maintainability of this situation are at the center ofthe 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 toproblems that may arise. This has led planners to decision support systems and forecasting models. In thispaper, three machine learnings algoritms, namely Support Vector Regression (SVR), Decision TreeRegression (DT), and Gaussian Process Regression (GPR) are employed to design a forecasting model forHealth 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 performancemetrics, 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 testingstage, respectively. Therefore, the SVR model has accurate prediction results with the highest R2and the leastroot mean square error values in the testing phase. The study showed that the proposed SVR model reducedRMSE value by 32.02% and 39.66% compared to the GPR and DT models, respectively. Consequently, theHealth Spendings of Turkey can be predicted by employing SVR with high accuracy.
Açıklama
Anahtar Kelimeler
Kaynak
Acta Infologica
WoS Q Değeri
Scopus Q Değeri
Cilt
5
Sayı
1
