Refining Medical Insurance Cost Predictions with Advanced Machine Learning Models

Sheying Shen

2025

Abstract

This paper employs diverse machine learning algorithms to enhance the accuracy of medical insurance cost predictions. With the increasing complexity of healthcare costs and the need for fair and sustainable pricing strategies, improving predictive accuracy is essential for both insurers and policyholders. Traditional methods often fail to capture the intricate relationships between various factors and medical costs, leading to suboptimal pricing. To address this, this paper leverages advanced machine learning techniques, including ensemble methods like XGBoost and Random Forest, to analyse real-world data. These methods not only improve prediction accuracy but also provide valuable insights into the key drivers of medical costs, such as lifestyle behaviours and health indicators. Results show ensemble methods like XGBoost and so on excel in predictive accuracy and generalization as well as offer insights into feature impacts, highlighting the substantial influence of behavioural and health indicators on pricing. The research concludes that these advanced techniques can significantly improve prediction precision, aiding insurers in refining their pricing strategies. It also underscores the role of model interpretability in financial risk management.

Paper Citation

in Harvard Style

Shen S. (2025). Refining Medical Insurance Cost Predictions with Advanced Machine Learning Models. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 573-580. DOI: 10.5220/0013702200004670

in Bibtex Style

@conference{icdse25,
author={Sheying Shen},
title={Refining Medical Insurance Cost Predictions with Advanced Machine Learning Models},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={573-580},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013702200004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Refining Medical Insurance Cost Predictions with Advanced Machine Learning Models
SN - 978-989-758-765-8
AU - Shen S.
PY - 2025
SP - 573
EP - 580
DO - 10.5220/0013702200004670
PB - SciTePress