Health Insurance Premium Pricing Using Machine Learning Methods

Abstract

Health insurance is important alongside life insurance products. This product’s subscription is gradually rising and has become one of the public’s main considerations due to their awareness on medical and surgical costs. This study is aimed at (i) identifying whether the independent factors are important in predicting the dependent variable, and (ii) to determine which model (logistic regression model/decision trees/neural networks) is the best model to be utilised. The SAS Enterprise Miner (E-Miner) was used to analyse the data and to select the best model. At the end of this study, the measurements like the Maximum Absolute Error (MAE), Average Squared Error (ASE), Root Average Squared Error (RASE), and Sum Squared Error (SSE) in the decision tree model indicated the lowest errors and led to the selection of the decision tree as the best model to be used in health insurance premium pricing.