LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq

Rizgar R. Zebari, Gheyath M. Zebari (), Adel Al-zebari and Marwan Aziz Mohammed
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Rizgar R. Zebari: Knowledge University
Gheyath M. Zebari: Akre University for Applied Sciences
Adel Al-zebari: Akre University for Applied Sciences
Marwan Aziz Mohammed: Knowledge University

SN Operations Research Forum, 2025, vol. 6, issue 1, 1-22

Abstract: Abstract A safe and adequate blood supply is essential for healthcare systems to function effectively. Accurately forecasting blood demand plays a key role in efficient inventory management and resource allocation. Traditional forecasting methods, like moving averages and ARIMA models, often fall short due to the complexity of factors influencing blood demand. This study introduces an innovative hybrid ensemble model that combines Long Short-Term Memory (LSTM) networks with XGBoost, harnessing their combined strengths to enhance forecasting accuracy. By analyzing blood donation data from the Zakho Blood Bank (January 1, 2015—July 22, 2022), the model outperforms individual LSTM and XGBoost models, excelling in metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE). These findings underscore the potential of advanced machine learning techniques to improve healthcare supply chain management and ensure the timely availability of critical blood supplies.

Keywords: Blood banking; Blood demand forecasting; Healthcare supply chain; LSTM network; Machine learning; XGBoost (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-024-00413-w

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