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COMPARISON OF CLASSICAL REGRESSION METHODS WITH DATA MINING REGRESSION METHODS IN PREDICTION OF HEALTH EXPENDITURE

Songl INAROLU.

Abstract
Aim: One of the main difficulties about analyzing health expenditures is, the distribution of health expenditure is not normal and extremely positively skewed. This brings about overfitting problem and causes a decrease in regression model performance for predicting health expenditures. It is possible to use data mining based regression methods to improve classical regression model performances and overcome overfitting problem. Regression Tress, Random Forest Regression and Support Vector Regression are some of these methods. In this study it is aimed to compare prediction performances of different regression methods about predicting per capita health expenditures of member of total 214 World Bank countries. Materials and Methods: Before the analysis the distribution of health expenditure per capita normalized with using logarithmic and Box-Cox transformations. Multiple Linear Regression, Regression Tree, Random Forest Regression and Support Vector Machine Regression methods was used for prediction and R2, RMSE and MAE values are used for the assessment of prediction performances. Performance results are compared according to cross validation values determined by using different number of k parameters. Findings: Study findings show that prediction performance of Support Vector Regression is relatively higher compared with other regression methods when health expenditure per capita transformed by using Box-Cox transformation and when k parameter increases in cross validation. Results: Study results show that Support Vector Regression prediction performance is higher than other regression methods. It is advisable for future studies to examine Support Vector Regression performances using grid search methods which are one of hyperparameter optimization techniques.

Key words: Multiple Linear Regression; Regression Tree; Random Forest Regression; Support Vector Regression; Health Expenditure per capita . JEL Codes: C10, C88, H51.



SALIK HARCAMASININ TAHMNNDE KLASK REGRESYON YNTEMLER LE VER MADENCL REGRESYON YNTEMLERNN KARILATIRILMASI

Özet
Ama: Salk harcamalar ile ilgili analizlerde karlalan temel glklerden birisi salk harcamas dalmnn normal dalm zellii gstermeyerek ar saa arpk olmasdr. Bu durum salk harcamalarn incelemek amacyla oluturulan regresyon modellerinde dorusallktan ayrlmay beraberinde getirmekte ve regresyon modelinin performansnn dmesine neden olmaktadr. Klasik regresyon modellerinin performans sonularn iyiletirmek amacyla veri madencilii temeline dayanan regresyon yntemlerinin kullanm sayesinde ar uyum sorununun stesinden gelinebilmektedir. Regresyon aac, Random Forest Regresyonu ve Destek Vektr Regresyonu bu yaklamlardan bazlardr. Bu almada 2013 yl itibariyle Dnya Bankasna ye olan toplam 214 lkeye ait veriler incelenerek kii ba salk harcamasnn tahmin edilmesine ynelik farkl regresyon yntemi performans sonularnn karlatrlmas amalanmtr. Gere ve Yntem: Analiz ncesinde salk harcamas deikenine ait dalm logaritmik ve Box-Cox dnmleri uygulanarak normalletirilmitir. almada salk harcamalarn tahmin etmek amacyla oklu Dorusal Regresyon, Regresyon Aac, Random Forest Regresyon ve Destek Vektr Regresyonu yntemleri kullanlmtr. Tahmin performanslarnn deerlendirilmesi amacyla R2, RMSE ve MAE deerlerinden yararlanlmtr. Performans sonular farkl saylarda belirlenen k parametrelerinden elde edilen apraz geerlilik deerleri zerinden karlatrlmtr. Bulgular: Elde edilen bulgular kii ba salk harcamas deikenine Box-Cox dnm uygulandnda ve apraz geerlilikte k parametresi arttrldnda Destek Vektr Regresyonu kullanlarak elde edilen performans sonularnn dier regresyon yntemlerine gre greceli olarak daha iyi tahmin gcne sahip olduunu ortaya koymaktadr. Sonu: alma sonular dier regresyon yntemlerine gre Destek Vektr Regresyonunun daha iyi performans sergilediini gstermektedir. lerleyen aratrmalar iin grid arama metodlarnn kullanld hiperparametre optimizasyon yntemlerinden yararlanlarak Destek Vektr Regresyonu performansnn daha detayl olarak incelenmesi tavsiye edilmektedir.

Anahtar Kelimeler: oklu Dorusal Regresyon; Regresyon Aac; Random Forest Regresyon; Destek Vektr Regresyonu; Kii Ba Salk Harcamas. JEL Kodlar: C10, C88, H51.


 
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How to Cite this Article
Pubmed Style

Songul CINAROGLU. [COMPARISON OF CLASSICAL REGRESSION METHODS WITH DATA MINING REGRESSION METHODS IN PREDICTION OF HEALTH EXPENDITURE]. Ekonomik Yaklasim. 2016; 27(101): 185-218. Turkish. doi:10.5455/ey.35947


Web Style

Songul CINAROGLU. [COMPARISON OF CLASSICAL REGRESSION METHODS WITH DATA MINING REGRESSION METHODS IN PREDICTION OF HEALTH EXPENDITURE]. http://www.ekonomikyaklasim.org/?mno=223658 [Access: December 17, 2017]. Turkish. doi:10.5455/ey.35947


AMA (American Medical Association) Style

Songul CINAROGLU. [COMPARISON OF CLASSICAL REGRESSION METHODS WITH DATA MINING REGRESSION METHODS IN PREDICTION OF HEALTH EXPENDITURE]. Ekonomik Yaklasim. 2016; 27(101): 185-218. Turkish. doi:10.5455/ey.35947



Vancouver/ICMJE Style

Songul CINAROGLU. [COMPARISON OF CLASSICAL REGRESSION METHODS WITH DATA MINING REGRESSION METHODS IN PREDICTION OF HEALTH EXPENDITURE]. Ekonomik Yaklasim. (2016), [cited December 17, 2017]; 27(101): 185-218. Turkish. doi:10.5455/ey.35947



Harvard Style

Songul CINAROGLU (2016) [COMPARISON OF CLASSICAL REGRESSION METHODS WITH DATA MINING REGRESSION METHODS IN PREDICTION OF HEALTH EXPENDITURE]. Ekonomik Yaklasim, 27 (101), 185-218. Turkish. doi:10.5455/ey.35947



Turabian Style

Songul CINAROGLU. 2016. [COMPARISON OF CLASSICAL REGRESSION METHODS WITH DATA MINING REGRESSION METHODS IN PREDICTION OF HEALTH EXPENDITURE]. Ekonomik Yaklasim, 27 (101), 185-218. Turkish. doi:10.5455/ey.35947



Chicago Style

Songul CINAROGLU. "[COMPARISON OF CLASSICAL REGRESSION METHODS WITH DATA MINING REGRESSION METHODS IN PREDICTION OF HEALTH EXPENDITURE]." Ekonomik Yaklasim 27 (2016), 185-218. Turkish. doi:10.5455/ey.35947



MLA (The Modern Language Association) Style

Songul CINAROGLU. "[COMPARISON OF CLASSICAL REGRESSION METHODS WITH DATA MINING REGRESSION METHODS IN PREDICTION OF HEALTH EXPENDITURE]." Ekonomik Yaklasim 27.101 (2016), 185-218. Print.Turkish. doi:10.5455/ey.35947



APA (American Psychological Association) Style

Songul CINAROGLU (2016) [COMPARISON OF CLASSICAL REGRESSION METHODS WITH DATA MINING REGRESSION METHODS IN PREDICTION OF HEALTH EXPENDITURE]. Ekonomik Yaklasim, 27 (101), 185-218. Turkish. doi:10.5455/ey.35947



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