Comparison of Machine Learning Regression Methods to Predict Health Expenditures

Yıl 2017, , 179 - 200, 19.09.2017

Songül Çınaroğlu

https://doi.org/10.17482/uumfd.338805

Cited By: 9

Öz

As a result of
experimental studies on different datasets, it is recommended to use machine
learning regression methods as an alternative to classical regression methods
in the existence of variables which are difficult to model. Health expenditure
is an indicator which is difficult to model and there is no study in the
literature about modelling health expenditure comparing machine learning
regression methods. In this study a
multiple regression model was conducted to predict health expenditure per
capita. Performance results of Lasso Regression, Random Forest Regression and
Support Vector Machine Regression compared when different hyperparameter values
were determined. Lambda (λ) value for Lasso Regression, number of trees for
Random Forest Regression, epsilon () value for Support Vector Regression was determined as hyperparameter
values. Study results performed by using “k” fold cross validation changed from
5 to 50, indicate the difference between machine learning results in terms of R²,
RMSE and MAE values that are statistically significant (p<0.001). Surface and bar plots and statistical test
results of prediction performances show that Random Forest Regression (R²
˃ 0.7500, RMSE ≤ 0.6000 ve MAE ≤ 0.4000) has better prediction performance
according to different hyperparameter values. It is hoped that study results
make contribution to studies about determining optimal hyperparameter values
for machine learning regression methods for studies about modelling health
expenditures. 

Anahtar Kelimeler

Machine Learning, Lasso Regression, Random Forest Regression, Support Vector Regression, Health Expenditure

SAĞLIK HARCAMASININ TAHMİNİNDE MAKİNE ÖĞRENMESİ REGRESYON YÖNTEMLERİNİN KARŞILAŞTIRILMASI

Öz

Farklı veri setleri üzerinde yapılan uygulamalar
sonucunda modellenmesi zor olan değişkenlerin varlığında klasik regresyon
yöntemlerine alternatif olarak makine öğrenmesi regresyon yöntemlerinin
kullanımı tavsiye edilmektedir. Sağlık harcaması modellenmesi zor olan bir
değişken olup, literatürde makine öğrenmesi regresyon yöntemleri karşılaştırılarak
bu değişkenin modellendiği bir çalışmaya rastlanmamıştır. Bu çalışmada kişi
başı sağlık harcamasının tahmini amacıyla bir çoklu regresyon modeli
oluşturulmuştur. Farklı hiperparametre değerleri belirlendiğinde elde edilen
Lasso Regresyon, Rastgele Ağaç Regresyonu ile Destek Vektör Makinesi Regresyon
performans sonuçları karşılaştırılmıştır. Çalışmada hiperparametre değeri
olarak Lasso Regresyon için lamda (λ) değeri, Rastgele Ağaç Regresyonu için
ağaç sayısı, Destek Vektör Regresyonu için epsilon () değeri esas alınmıştır. Sonuçlar 5 ile 50
arasında değişen “k” parça çapraz geçerlilik uygulanarak performe edildiğinde makine
öğrenmesi regresyon yöntemlerine ait performans sonuçlarının R²,
RMSE ve MAE değerleri bakımından istatistiksel olarak anlamlı farklılıklar gösterdiği
(p<0.001) tespit
edilmiştir. Tahmin performanslarına ait yüzey ve çubuk grafikleri ile
istatistiksel test sonuçları incelendiğinde farklı hiperparametre değerlerine
göre Rastgele Ağaç Regresyonun (R² ˃ 0.7500, RMSE ≤ 0.6000 ve MAE ≤
0.4000) daha iyi tahmin sonuçlarına sahip olduğu belirlenmiştir. Çalışma
sonuçlarının, sağlık harcamasının modellendiği araştırmalar için makine
öğrenmesi regresyon yöntemleri kullanıldığında en uygun hiperparametre
değerlerinin belirlenmesi konusunda katkı sağlaması beklenmektedir. 

Anahtar Kelimeler

Makine Öğrenmesi, Lasso Regresyon, Rastgele Ağaç Regresyonu, Destek Vektör Regresyonu, Sağlık Harcaması

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