# Machine Learning For An Explainable Cost Prediction of Medical Insurance

arXiv: https://arxiv.org/abs/2311.14139 · HTML: https://mlsanity.com/paper/2311.14139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 98 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
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- [Consistency of random forests](https://mlsanity.com/paper/1405.2881/) (2014)
- [Secure and Robust Machine Learning for Healthcare: A Survey](https://mlsanity.com/paper/2001.08103/) (2020)
- [Interpretability of machine learning based prediction models in healthcare](https://mlsanity.com/paper/2002.08596/) (2020)
- [General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models](https://mlsanity.com/paper/2007.04131/) (2020)
- [Visualizing the Feature Importance for Black Box Models](https://mlsanity.com/paper/1804.06620/) (2018)
- [Transparency: Motivations and Challenges](https://mlsanity.com/paper/1708.01870/) (2017)
- [Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms](https://mlsanity.com/paper/1809.04559/) (2018)
- [Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans](https://mlsanity.com/paper/2009.10990/) (2020)
- [TRBoost: A Generic Gradient Boosting Machine based on Trust-region Method](https://mlsanity.com/paper/2209.13791/) (2022)
- [Using massive health insurance claims data to predict very high-cost claimants: a machine learning approach](https://mlsanity.com/paper/1912.13032/) (2019)
- [Gradient boosting machine with partially randomized decision trees](https://mlsanity.com/paper/2006.11014/) (2020)
- [Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning](https://mlsanity.com/paper/2209.07980/) (2022)
- [Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak](https://mlsanity.com/paper/2305.19297/) (2023)

## Abstract (arXiv)

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.