Comparative regression performances of machine learning methods optimising hyperparameters: application to health expenditures Online publication date: Tue, 16-Feb-2021
by Songul Cinaroglu; Onur Baser
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 16, No. 4, 2020
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.
Online publication date: Tue, 16-Feb-2021
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Bioinformatics Research and Applications (IJBRA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org