Türkiye genelinde renal replasman tedavisine ihtiyaç duyacak olan hasta sayısının GM (1,1) ve OGM (1,1) ile tahmin edilmesi

Yıl 2021, Cilt: 14 Sayı: 1, 35 - 43, 30.04.2021

Tezcan Şahin Saffet Ocak

https://doi.org/10.26559/mersinsbd.765329

Öz

Amaç: 2018-2023 yılları arasında RRT tedavisi görmesi gerekecek hasta sayısını tahmin etmektir. Yöntem: Tahmin etme sürecinde genel olarak zaman serilerinin tahmin edilmesinde kullanılan bir yöntem olan gri tahmin etme yöntemleri kullanılmıştır. Gri sistemlerde tahmin edebilmek için çeşitli modeller geliştirilmiş olmakla birlikte bu çalışmada GM (1,1) ve OGM (1,1) modelleri kullanılmıştır. Verilerin analizinde Microsoft Excel 2016 tabanlı Genel İndirgenmiş Gradyan metodundan yararlanılmıştır. Araştırma verileri, 2006-2017 yılları arasında Türkiye’de RRT gören hasta sayılarından oluşmaktadır. Modellerin tahmin performansı ortalama mutlak yüzde hata (MAPE) ve kök ortalama kare hata (RMSE) ile ölçülmüştür. Bulgular: Karşılaştırmalar sonucunda OGM (1,1)’in (MAPE: %2.0 RMSE: 1484) GM (1,1) modeline (MAPE: %2.1 RMSE: 1740) göre daha iyi performans gösterdiği tespit edilmiştir. 2006-2017 verilerine dayanarak tahmin edilen ve gerçekleşen veriler bazında yakınsama oranları karşılaştırıldığında da OGM (1,1) modelinin daha başarılı olduğu belirlenmiştir. 2018-2023 yılları arasında RRT görecek hasta sayısındaki ortalama yıllık büyüme oranı, GM (1,1) modeline göre %4.12; OGM (1,1) modeline göre ise %4.64’tür. Bu modellere göre, hasta sayısı her yıl bir önceki yıla göre artış göstereceği tahmin edilmektedir. 2017’de 77311 olan hasta sayısı 2023 yılında OGM (1,1) modeline göre 104105’e ulaşacağı öngörülmektedir. Sonuç: Bu yükseliş nedeniyle insidansı gittikçe artma eğilimi gösteren kronik böbrek hastalığının önlenmesi ve topluma ve devlete sosyo-ekonomik yükünün azaltılması için etkili önlemler (renal transplantasyon, organ bağışının özendirilmesi vs.) alınması gerekliliği gün yüzüne çıkmaktadır

Anahtar Kelimeler

Hasta tahmini, Gri tahminleme yöntemi, Renal replasman tedavisi, Zaman serileri analizi

Prediction of number of patients will need to renal replacement therapy in Turkey with GM (1,1) and OGM (1,1)

Öz

Aim: The main purpose of the study is to estimate the number of patients requiring RRT treatment between 2018-2023. Method: In the estimation process, grey estimation methods, which are generally used for estimating time series, are used. Although various models have been developed to predict grey systems, GM (1,1) and OGM (1,1) models are used in this study. General Reduced Gradient method based on Microsoft Excel 2016 was used to analyze the data. Research data consists of the number of patients receiving RRT between the years 2006-2017 in Turkey. Estimated performance of the models was measured by mean absolute percent error (MAPE) and root mean square error (RMSE). Results: As a result of the comparisons, it was found that OGM (1,1) (MAPE: 2% RMSE: 1484) performed better than GM (1,1) (MAPE: 2.1% RMSE: 1740). When the convergence rates are compared on the basis of estimated and actual data based on 2006-2017, it is found that OGM (1,1) is more successful. The average annual growth rate of the number of patients who will see RRT between 2018-2023 is 4.12% according to GM (1,1) and 4.64% according to OGM (1,1). According to these data, the number of patients will increase each year compared to the previous year. The number of patients who were 77311 in 2017 will reach 104105 in 2023 according to OGM (1,1). Conclusion: Due to this increase, effective preventions (renal transplantation, promotion of organ donation, etc.) should be taken to prevent chronic kidney disease whose incidence tends to increase gradually and to reduce the socio-economic burden on society and the state.

Anahtar Kelimeler

Patient forecasting, Grey prediction model, Renal replacement therapy, time series analysis

Kaynakça

• Kaynaklar 1. Özcan Y. Sağlık Kurumları Yönetiminde Sayısal Yöntemler, Kavuncubaşı Ş, Yıldırım S, Çev. Ankara, Türkiye: Siyasal Kitabevi; 2013.
• 2. Dang HS, Huang YF, Wang CN, Nguyen TMT. An Application of the Short-Term Forecasting with Limited Data in the Healthcare Traveling Industry. Sustainability 2016; 8(1037):1-14.
• 3. Eggers PW. Has the incidence of end-stage renal disease in the USA and other countries stabilized?, Curr Opin Nephrol Hypertens 2011; 20(3):241-245.
• 4. Crews DC, Bello AK, Saadi G. Burden, Access, and Disparities in Kidney Disease, Turk Journal of Nephrology 2019;28(1): 1-7.
• 5. Türk Nefroloji Derneği, 2017 Yılı Türk Böbrek Kayıt Sistemi Raporu, Erişim: http://www.nefroloji.org.tr/folders/file/TND-2017-Kayit-Sistemi-Verileri.pdf Erişim Tarihi: 10.06.2019.
• 6. Satman I, Omer B, Tutuncu Y, et al. TURDEP-II Study Group: Twelve-year trends in the prevalence and risk factors of diabetes and prediabetes in Turkish adults, Eur J Epidemiol 2013;28 (2): 169-180.
• 7. Lei M, Feng Z. A Proposed grey model for short-term electricity price forecasting in competitive power markets, International Journal of Electrical Power & Energy Systems 2012;43(1): 531-538.
• 8. Yang X, Zou J, Kong D, Jiang G. The analysis of GM (1, 1) grey model to predict the incidence trend of typhoid and paratyphoid fevers in Wuhan City, China. Medicine (Baltimore) 2018; 97(34): 1-5.
• 9. Wang YW, Shen ZZ, Jiang Y. Comparison of ARIMA and GM (1,1) models for prediction of hepatitis b in china, PLoS ONE 2018; 13(9):e0201987. https://doi.org/10.1371/journal.pone.0201987.
• 10. Şahin U. Forecasting of Turkey’s electricity generation and consumption with grey prediction method. Mugla Journal of Science and Technology 2018; 4(2): 205-209.
• 11. Lin C.S., Liou F.M., Huang C.P. Grey forecasting model for CO2 emissions: A Taiwan study. Applied Energy 2011; 88(11): 2816-3820.
• 12. Huang Y.L. Forecasting the demand for health tourism in Asian countries using a GM (1,1)-alpha model. Tourism and Hospitality Management 2012; 18(2): 171-181.
• 13. Zang P, Jin Z. Prediction analysis of the prevalence of alzheimer’s disease in china based on meta analysis. Open Access Library Journal 2020; (7): 1-13. doi: 10.4236/oalib.1106375.
• 14. Öztürk Z, Bilgil H. Mathematical estimation of expenditures in the health sector in turkey with grey modeling. Erciyes Üniversitesi Fen Bilimleri Enstitüsü Dergisi 2019;35(3): 52-58.
• 15. Şahin T. Ruh sağlığı ve hastalıkları polikliniğine olan talebin zaman serileri modelleri ile tahmini. Hacettepe Sağlık İdaresi Dergisi 2019a;22(4): 749-764.
• 16. Iqelan BM. Forecasts of female breast cancer referrals using grey prediction model GM (1,1). Applied Mathematical Sciences 2017;11(54): 2647-2662.
• 17. Jounini R, Lemlouma T, Maalaoui K. Employing grey model forecasting GM (1,1) to historical medical sensor data towards system preventive in smart home e-health for elderly person, International Wireless Communications and Mobile Computing Conference, 2016;1086-1091.
• 18. Zhang L, Wang L, Zheng Y, Wang K, Zhang X, Zheng Y. Time prediction models for echinococcosis based on gray system theory and epidemic dynamics, Int J Environ Res Public Health 2017;14(262): 1-14.
• 19. Ene S., Öztürk N. Grey modelling based forecasting system for return flow of end-of-life vehicles. Technological Forecasting &Social Change 2017; 115: 155-166.
• 20. Ma W, Zhu X, Wang M. Forecasting iron ore import and consumption of China using greymodel optimized by particle swarm optimization algorithm, Resources Policy 2013; 38 (2013): 613–620.
• 21. Şahin U. Forecasting of Turkey's greenhouse gas emissions using linear and nonlinear rolling metabolic grey model based on optimization. Journal of Cleaner Production 2019b; 239: 118079.
• 22. Şahin U. Future of renewable energy consumption in France, Germany, Italy, Spain, Turkey and UK by 2030 using optimized fractional nonlinear grey Bernoulli model. Sustainable Production and Consumption 2021; 25: 1-14.
• 23. Ayvaz B, Kusakci AO. Electricity consumption forecasting for urkey with nonhomogeneous discrete grey model. Energy Sources, Part B: Economics, Planning, and Policy 2017;12(3): 260-267.
• 24. Agrawal RK, Muchahary F, Tripathi MM. Ensemble of relevance vector machines and boosted trees for electricity price forecasting. Applied Energy 2019;250(C): 540-548.
• 25. Falayi EO, Adepitan JO, Rabiu AB. Empirical models for the correlation of global solar radiation with meteorological data for Iseyin, Nigeria. International Journal of Physical Sciences 2008;3(9): 210-216.
• 26. Li Y, Shi H, Han F, Duan Z, Liu H. Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy. Renewable Energy 2019;135: 540-553.
• 27. Wen J.C., Huang K.H., Wen K.L. The study of α in GM (1,1) model. Journal of the Chinese Institute of Engineers 2000; 23(5): 583-589.