An empirical analysis of Bayesian-optimized boosting ensembles for medical relief demand forecasting
Roman B. Villones, Jonilo C. Mababa, Jovy Jay D. Cabrera & Jaime P. Pulumbarit
Abstract
In humanitarian logistics, the planning of medical relief supply is sensitive to accurate demand forecasting due to its intermittent and volatile demand pattern that constrain the usefulness of conventional statistical techniques. This paper provides an empirical analysis of the increase in ensemble learning models that are optimized through Bayesian searching of hyperparameters to predict demand of medical relief. Using the Team Data Science Process (TDSP) framework, a quantitative methodology is used to evaluate the performance of the model based on predictive accuracy and robustness performance with extreme values, and generalizability on a real-world dataset in the National Capital Region (NCR), Philippines. There are five ensemble models such as AdaBoost, CatBoost, Gradient Boosting, LightGBM, and XGBoost. Findings indicate that Bayesian optimization produces a tangible performance gain, especially on Gradient Boosting and LightGBM. The CatBoost model produces the lowest RMSE, WAPE, and MASE, and the most consistent cross-validation results, which means that it is more accurate and stable in the model tested. On the contrary, XGBoost and AdaBoost exhibits relatively poorer performance and low robustness. Although the results illustrate the efficiency of optimized boosting ensembles to handle complex and irregular demand shapes, the research study is limited by the coverage of datasets, possible temporal leakage and lack of actual deployment. Thus, it is possible to arrive at conclusions only based on the assessed data and experimental conditions.
Keywords
boosting ensemble learning, Bayesian hyperparameter optimization, medical relief supply chain, humanitarian logistics, machine learning
Author information & Contribution
Roman B. Villones. Corresponding author. Master in Information Technology. Graduate School Department, La Consolacion University Philippines, Philippines. Email: roman.villones@email.lcup.edu.ph
Jonilo C. Mababa. Doctor of Information Technology & PhD in Educational Leadership and Management. Graduate School Department, La Consolacion University Philippines, Philippines. Email: jonilo.mababa@email.lcup.edu.ph
Jovy Jay D. Cabrera. Doctor of Information Technology. Graduate School Department, La Consolacion University Philippines, Philippines. Email: jovyjay.cabrera@email.lcup.edu.ph
Jaime P. Pulumbarit. Doctor of Information Technology. Graduate School Department, La Consolacion University Philippines, Philippines. Email: jaime.pulumbarit@email.lcup.edu.ph
"All authors are contributed to the study. All authors reviewed and approved the final version of the manuscript."
Disclosure statement
The authors declare no conflict of interest.
Funding
This research did not receive any specific grant from any funding agencies.
Institutional Review Board Statement
Not Applicable
Data and Materials Availability
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
AI Declaration
The author declares no Artificial Intelligence–based writing used in the preparation of this manuscript. All analyses, model development, and result generation were conducted using Python programming within the Jupyter Notebook environment. The author takes full responsibility for the integrity and originality of the work.
Notes
Acknowledgement
The researchers are gratefully acknowledging the community in the National Capital Region (NCR), Philippines, for providing access to the datasets that were essential for this study. The researchers also extend their sincere appreciation to the Graduate School of La Consolacion University Philippines for its continuous support and guidance throughout the research process.
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Cite this article:
Villones, R.B., Mababa, J.C., Cabrera, J.J.D. & Pulumbarit, J.P. (2026). An empirical analysis of Bayesian-optimized boosting ensembles for medical relief demand forecasting. International Journal of Science, Technology, Engineering and Mathematics, 6(2), 1-21. https://doi.org/10.53378/ijstem.353345
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