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

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 (e) 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 0.79; 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.