Enhancing Medical Expenditure Prediction Using Machine Learning on Claims Data for Better Healthcare Cost Management

DOI

10.2991/978-94-6463-938-4_54How to use a DOI?

Keywords

Claim Data; Linear Regression; Medical Expenditure; Random Forest; XGBoost

Abstract

The prospective disease group-based payment system, implemented in frameworks such as APR-DRG, seeks to standardize claims management but encounters several limitations. This study aims to enhance the accuracy of health insurance reimbursement claims by leveraging health claims data and machine learning techniques, specifically Extreme Gradient Boosting (XGBoost), Random Forest, and Linear Regression. The research identifies that the precision of health cost claims utilizing the APR-DRG coding system and CCS diagnosis codes can be significantly improved. By incorporating additional variables from claims data, such as demographics, diagnoses, and facility utilization, the study develops a robust predictive model for medical expenditures. The findings demonstrate that both XGBoost and Random Forest algorithms outperform traditional linear regression, providing high accuracy in predicting inpatient costs. This advancement has the potential to improve health cost management and reduce discrepancies in reimbursement processes. Future research should expand the predictive variables to include comorbidities and explore other coding systems, such as INA-CBG, to further enhance the accuracy of healthcare cost predictions in diverse contexts.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rosiyah Faradisa
AU  - Yustria Mahendra Akbar
AU  - Yuliana Setiowati
AU  - Tessy Badriyah
AU  - Mohammad Hasbi Assidiqi
PY  - 2025
DA  - 2025/12/31
TI  - Enhancing Medical Expenditure Prediction Using Machine Learning on Claims Data for Better Healthcare Cost Management
BT  - Proceedings of the International Conference on Applied Science and Technology on Social Science 2025 (iCAST-SS 2025
PB  - Atlantis Press
SP  - 473
EP  - 481
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-938-4_54
DO  - 10.2991/978-94-6463-938-4_54
ID  - Faradisa2025
ER  -