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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rizgar R. Zebari & Gheyath M. Zebari & Adel Al-zebari & Marwan Aziz Mohammed, 2025. "LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq," SN Operations Research Forum, Springer, vol. 6(1), pages 1-22, March.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:1:d:10.1007_s43069-024-00413-w
    DOI: 10.1007/s43069-024-00413-w
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    References listed on IDEAS

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    1. Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K, 2019. "Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda," International Journal of Information Management, Elsevier, vol. 48(C), pages 63-71.
    2. Soheyl Khalilpourazari & Hossein Hashemi Doulabi, 2023. "A flexible robust model for blood supply chain network design problem," Annals of Operations Research, Springer, vol. 328(1), pages 701-726, September.
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