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.

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