Article: Comparative regression performances of machine learning methods optimising hyperparameters: application to health expenditures Journal: International Journal of Bioinformatics Research and Applications (IJBRA) 2020 Vol.16 No.4 pp.387 - 407 Abstract: Machine learning (ML) algorithms are used in various areas. However, there has been no study analysing health expenditures using ML methods. This work is a step forward in comparing the regression performances of lasso (L), K-nearest neighbourhood (KNN), Random Forest (RF) and support vector machine (SVM) regression while changing hyperparameter values. In this study, lambda (λ), number of neighbours (NN), number of trees (NT) and epsilon (ε) parameter for L, KNN, RF and SVM regression were determined as hyperparameters, respectively. K-fold cross-validation was performed to examine regression performance results. Study results show that KNN (R2 > 0.75; RMSE < 0.70; MAE < 0.55) and L (R2 > 0.79; RMSE < 0.20; MAE < 0.15) regression yields better results in predicting health expenditure per capita and out-of-pocket health expenditure (%) respectively. Moreover, L, KNN, RF and SVM regression methods performance differences are statistically significant (p < 0.001). It is hoped that these results will stimulate further interest in using ML methods to predict health expenditures. Inderscience Publishers - linking academia, business and industry through research

Title: Comparative regression performances of machine learning methods optimising hyperparameters: application to health expenditures

Authors: Songul Cinaroglu; Onur Baser

Addresses: Faculty of Economics & Administrative Sciences, Hacettepe University, Department of Health Care Management, Ankara, Turkey ' Columbia University, Department of Surgery, Center for Innovation and Outcomes Research, New York, NY, USA; STATinMED Research, New York, NY, USA

Abstract: Machine learning (ML) algorithms are used in various areas. However, there has been no study analysing health expenditures using ML methods. This work is a step forward in comparing the regression performances of lasso (L), K-nearest neighbourhood (KNN), Random Forest (RF) and support vector machine (SVM) regression while changing hyperparameter values. In this study, lambda (λ), number of neighbours (NN), number of trees (NT) and epsilon (ε) parameter for L, KNN, RF and SVM regression were determined as hyperparameters, respectively. K-fold cross-validation was performed to examine regression performance results. Study results show that KNN (R² > 0.75; RMSE < 0.70; MAE < 0.55) and L (R² > 0.79; RMSE < 0.20; MAE < 0.15) regression yields better results in predicting health expenditure per capita and out-of-pocket health expenditure (%) respectively. Moreover, L, KNN, RF and SVM regression methods performance differences are statistically significant (p < 0.001). It is hoped that these results will stimulate further interest in using ML methods to predict health expenditures.

Keywords: machine learning; lasso regression; nearest neighbours regression; random forest regression; support vector machine regression; hyperparameter optimisation; black-box optimisation; health expenditures.

DOI: 10.1504/IJBRA.2020.113022

International Journal of Bioinformatics Research and Applications, 2020 Vol.16 No.4, pp.387 - 407

Accepted: 03 Feb 2018
Published online: 16 Feb 2021 *

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Title: Comparative regression performances of machine learning methods optimising hyperparameters: application to health expenditures

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