Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models

Didem GÜLERYÜZ (Bayburt Üniversitesi, Uygulamalı Bilimler Fakültesi, Yönetim Bilişim Sistemleri Bölümü, Bayburt, Türkiye)
Acta Infologica
6 4
Yıl: 2021 Cilt: 5 Sayı: 1 Sayfa Aralığı: 155 - 166 Metin Dili: İngilizce DOI: 10.26650/acin.882660 İndeks Tarihi: 25-11-2021

Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models

Öz:
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.
Anahtar Kelime:

Türkiye Sağlık Harcamalarının GPR, SVR ve DT Modelleri ile Tahmini

Öz:
Ülkeler için artan sağlık maliyetleri ve bu durumun uzun vadeli sürdürülebilirliği siyasi gündeminmerkezinde yer almaktadır. Sağlık harcamalarındaki sürekli artış, hükümet bütçeleri, sağlık hizmetleri vekişisel hasta finansmanı üzerinde baskı oluşturmaktadır. Politika yapıcılar, ortaya çıkabilecek sorunlarauyum sağlamak ve bu maliyetleri düşürmek için reformlar planlamak isterler. Bu durum, planlayıcıları karardestek 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 öğrenmealgoritması kullanılmıştır. Kişi başına gayri safi yurtiçi hasıla, kentsel nüfus oranı, işsizlik oranı, 65 yaş veüstü nüfus, ortalama yaşam süresi, hekim oranı ve toplam hastane yatak sayısı girdi değişkenleri olarakbelirlenmiştir. 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 ve0.9611, test aşamasında sırasıyla 0.9536, 0.8944 ve 0.1166’dır. Ayrıca, SVR modeli, test aşamasında enyüksek R2ve 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 % 39.66azalttığını göstermiştir. Sonuç olarak, Türkiye’nin sağlık harcamaları SVR modeli kullanılarak yüksekdoğrulukta tahmin edilebilir.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA GULERYUZ D (2021). Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models. , 155 - 166. 10.26650/acin.882660
Chicago GULERYUZ DIDEM Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models. (2021): 155 - 166. 10.26650/acin.882660
MLA GULERYUZ DIDEM Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models. , 2021, ss.155 - 166. 10.26650/acin.882660
AMA GULERYUZ D Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models. . 2021; 155 - 166. 10.26650/acin.882660
Vancouver GULERYUZ D Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models. . 2021; 155 - 166. 10.26650/acin.882660
IEEE GULERYUZ D "Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models." , ss.155 - 166, 2021. 10.26650/acin.882660
ISNAD GULERYUZ, DIDEM. "Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models". (2021), 155-166. https://doi.org/10.26650/acin.882660
APA GULERYUZ D (2021). Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models. Acta Infologica, 5(1), 155 - 166. 10.26650/acin.882660
Chicago GULERYUZ DIDEM Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models. Acta Infologica 5, no.1 (2021): 155 - 166. 10.26650/acin.882660
MLA GULERYUZ DIDEM Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models. Acta Infologica, vol.5, no.1, 2021, ss.155 - 166. 10.26650/acin.882660
AMA GULERYUZ D Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models. Acta Infologica. 2021; 5(1): 155 - 166. 10.26650/acin.882660
Vancouver GULERYUZ D Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models. Acta Infologica. 2021; 5(1): 155 - 166. 10.26650/acin.882660
IEEE GULERYUZ D "Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models." Acta Infologica, 5, ss.155 - 166, 2021. 10.26650/acin.882660
ISNAD GULERYUZ, DIDEM. "Predicting Health Spending in Turkey Using theGPR, SVR, and DT Models". Acta Infologica 5/1 (2021), 155-166. https://doi.org/10.26650/acin.882660
Sayfa Oluşturulma Tarihi
23-04-2024 20:18

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