Revolutionizing Blood Bank Management: Leveraging Machine Learning for Inventory Optimization and Shortage Prediction
Ajit Pal Singh *
Department of Medical Lab Technology, SSAHS, Sharda University, Gr. Noida, U.P, India.
Riya Pandey
Paramedical School, GIMS Medical Laboratory Technology, Greater Noida, India.
Rahul Saxena
Department of Biochemistry, SSAHS, Sharda University, Gr. Noida, U.P, India.
Suyash Saxena
Department of Biochemistry, SSAHS, Sharda University, Gr. Noida, U.P, India.
*Author to whom correspondence should be addressed.
Abstract
This study proposes a novel machine learning framework to enhance blood bank management, focusing on inventory optimization and shortage prediction. By leveraging big data analytics, the model identifies donor trends, seasonal fluctuations, and real-time hospital demand to dynamically adjust blood supply. The predictive accuracy of our machine learning model surpasses traditional heuristic approaches, ensuring timely blood availability while minimizing wastage. These findings offer a transformative step toward data-driven healthcare logistics, with direct implications for global blood supply chain efficiency.
Keywords: Blood bank, big data analytics, machine learning, blood shortage prediction, inventory management, donor recommendation