International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 5 (September-October 2026) Submit your research before last 3 days of October to publish your research paper in the issue of September-October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 5
September-October 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

A Machine Learning-Based Analysis of Health Insurance Expenditures in Rwanda

Author(s) Mr. Ephraim UYISENGA, Dr. Roger MUREMYI
Country Rwanda
Abstract Background: Rising healthcare costs among elderly insured members present
sustainability challenges for Rwanda’s health insurance schemes. This study applies machine learning (ML) to predict healthcare expenditures for elderly members, identify key cost drivers, and develop a deployable actuarial decision-making tool.
Objective: To analyze and predict health expenditure patterns of elderly insured
members using ML, compare predictive models, and provide policy-relevant insights.
Methods: Claims data (2022–2024) from the Military Medical Insurance (MMI) scheme were analyzed using Multiple Linear Regression, Random Forest, Gradient Boosting, and XGBoost. Data were split into training (80%) and testing (20%) sets. Models were evaluated using R², MAE, and RMSE. SHAP values were used for interpretability. Results: Ensemble models outperformed linear models; Random Forest achieved the highest accuracy (R² = 0.74). Age, BMI, and service categories (Acts, imaging, and hospitalization) were major predictors. NCDs affected costs indirectly through increased use of high-cost services. Conclusion: ML can improve risk adjustment, preventive targeting, and pricing strategies in Rwanda’s health insurance.
Keywords Machine Learning, Health Insurance, Elderly, Healthcare Expenditure Prediction, Rwanda, Actuarial Science, Random Forest, XGBoost
Field Computer > Data / Information
Published In Volume 8, Issue 5, September-October 2026
Published On 2026-10-21
DOI https://doi.org/10.36948/ijfmr.2026.v08i05.87218
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals