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.

翻译：医疗保健领域的预测建模持续成为精算研究的活跃课题，众多保险公司正致力于充分发挥机器学习方法的潜力以提升生产力与运营效率。本文采用三种基于回归的集成机器学习模型——通过极端梯度提升、梯度提升机和随机森林方法融合决策树变体——来预测医疗保险费用。应用可解释人工智能方法中的SHAP值和个体条件期望图，揭示并解析影响数据集中医疗保险保费定价的关键决定因素。本研究使用的数据集包含986条记录，来自KAGGLE公共数据库。模型评估采用四个性能评价指标，包括决定系数、平均绝对误差、均方根误差和平均绝对百分比误差。结果表明，所有模型均取得优异成效；然而，XGBoost模型虽消耗更多计算资源但整体性能更优，而RF模型在预测误差更低的同时计算资源消耗远少于XGBoost模型。此外，我们比较了两种XAI方法在识别影响各模型保费定价关键特征方面的效果：尽管两者得出相似结论，但ICE图能更详细地展示各变量间的交互作用，而SHAP分析则更侧重于宏观层面的解释。本研究旨在通过研究成果助力政策制定者、保险机构及潜在医疗投保人在选择符合个性化需求的合适保单时进行科学决策。