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

Songül ÇINAROĞLU.

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.



SAĞLIK HARCAMASININ TAHMİNİNDE KLASİK REGRESYON YÖNTEMLERİ İLE VERİ MADENCİLİĞİ REGRESYON YÖNTEMLERİNİN KARŞILAŞTIRILMASI

Özet
Amaç: Sağlık harcamaları ile ilgili analizlerde karşılaşılan temel güçlüklerden birisi sağlık harcaması dağılımının normal dağılım özelliği göstermeyerek aşırı sağa çarpık olmasıdır. Bu durum sağlık harcamalarını incelemek amacıyla oluşturulan regresyon modellerinde doğrusallıktan ayrılmayı beraberinde getirmekte ve regresyon modelinin performansının düşmesine neden olmaktadır. Klasik regresyon modellerinin performans sonuçlarını iyileştirmek amacıyla veri madenciliği temeline dayanan regresyon yöntemlerinin kullanımı sayesinde aşırı uyum sorununun üstesinden gelinebilmektedir. Regresyon ağacı, Random Forest Regresyonu ve Destek Vektör Regresyonu bu yaklaşımlardan bazılarıdır. Bu çalışmada 2013 yılı itibariyle Dünya Bankası’na üye olan toplam 214 ülkeye ait veriler incelenerek kişi başı sağlık harcamasının tahmin edilmesine yönelik farklı regresyon yöntemi performans sonuçlarının karşılaştırılması amaçlanmıştır. Gereç ve Yöntem: Analiz öncesinde sağlık harcaması değişkenine ait dağılım logaritmik ve Box-Cox dönüşümleri uygulanarak normalleştirilmiştir. Çalışmada sağlık harcamalarını tahmin etmek amacıyla Çoklu Doğrusal Regresyon, Regresyon Ağacı, Random Forest Regresyon ve Destek Vektör Regresyonu yöntemleri kullanılmıştır. Tahmin performanslarının değerlendirilmesi amacıyla R2, RMSE ve MAE değerlerinden yararlanılmıştır. Performans sonuçları farklı sayılarda belirlenen “k” parametrelerinden elde edilen çapraz geçerlilik değerleri üzerinden karşılaştırılmıştır. Bulgular: Elde edilen bulgular kişi başı sağlık harcaması değişkenine Box-Cox dönüşümü uygulandığında ve çapraz geçerlilikte “k” parametresi arttırıldığında Destek Vektör Regresyonu kullanılarak elde edilen performans sonuçlarının diğer regresyon yöntemlerine göre göreceli olarak daha iyi tahmin gücüne sahip olduğunu ortaya koymaktadır. Sonuç: Çalışma sonuçları diğer regresyon yöntemlerine göre Destek Vektör Regresyonunun daha iyi performans sergilediğini göstermektedir. İlerleyen araştırmalar için grid arama metodlarının kullanıldığı hiperparametre optimizasyon yöntemlerinden yararlanılarak Destek Vektör Regresyonu performansının daha detaylı olarak incelenmesi tavsiye edilmektedir.

Anahtar Kelimeler: Çoklu Doğrusal Regresyon; Regresyon Ağacı; Random Forest Regresyon; Destek Vektör Regresyonu; Kişi Başı Sağlık Harcaması. JEL Kodları: C10, C88, H51.


 
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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]. www.scopemed.org/?mno=223658 [Access: June 29, 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 June 29, 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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