Comparison and Analysis of the Effectiveness of Linear Regression, Decision Tree, and Random Forest Models for Health Insurance Premium Forecasting

Abstract

Health insurance is a type of insurance that covers individual and family medical expenses and is important for the health and financial security of individuals and families. To better predict the demand for health insurance, three regression models in machine learning - random forest, linear regression, and decision tree - are widely used for health insurance prediction. Among these three regression models, random forest regression has the best prediction effect with a model score of 0.8564, which is the best prediction effect among the three models. Random forest regression is an integrated learning method that combines multiple decision tree models into a more powerful model that can effectively avoid overfitting problems and can handle large amounts of data. Therefore, random forest regression is a very effective method for health insurance prediction. The next model is the linear regression model with a model score of 0.7584. The linear regression model is a basic regression model that can be used to predict a linear relationship between two variables. In health insurance prediction, linear regression modeling can be used to predict the linear relationship between health insurance costs and related factors such as age, gender, and illness. The worst predictor was the decision tree, with a model score of 0.7097. The decision tree model can be used in Medicare forecasting to predict nonlinear relationships between Medicare costs and related factors such as age, gender, and illness.