  Predictive modeling in healthcare continues to be an active actuarial
research topic as more insurance companies aim to maximize the potential of
Machine Learning approaches to increase their productivity and efficiency. In
this paper, the authors deployed three regression-based ensemble ML models that
combine variations of decision trees through Extreme Gradient Boosting,
Gradient-boosting Machine, and Random Forest) methods in predicting medical
insurance costs. Explainable Artificial Intelligence methods SHapley Additive
exPlanations and Individual Conditional Expectation plots were deployed to
discover and explain the key determinant factors that influence medical
insurance premium prices in the dataset. The dataset used comprised 986 records
and is publicly available in the KAGGLE repository. The models were evaluated
using four performance evaluation metrics, including R-squared, Mean Absolute
Error, Root Mean Squared Error, and Mean Absolute Percentage Error. The results
show that all models produced impressive outcomes; however, the XGBoost model
achieved a better overall performance although it also expanded more
computational resources, while the RF model recorded a lesser prediction error
and consumed far fewer computing resources than the XGBoost model. Furthermore,
we compared the outcome of both XAi methods in identifying the key determinant
features that influenced the PremiumPrices for each model and whereas both XAi
methods produced similar outcomes, we found that the ICE plots showed in more
detail the interactions between each variable than the SHAP analysis which
seemed to be more high-level. It is the aim of the authors that the
contributions of this study will help policymakers, insurers, and potential
medical insurance buyers in their decision-making process for selecting the
right policies that meet their specific needs.
