Optimizing Blood Donor Management System in Algeria

Abstract

Blood supply chain management plays a vital role in maintaining the availability, qual ity, and safety of blood products, particularly in developing healthcare systems. This thesis proposes and evaluates a smart, AI-powered platform for blood donor manage ment in Algeria, integrating predictive analytics, donor classification, and scheduling optimization. The platform was deployed and tested in a major blood donation center in Algiers, using data from 2017 to 2021. Through the application of machine learn ing algorithms such as Random Forest, ARIMA, and SVM, the system demonstrated improvements in forecasting blood demand (with a MAPE of 6.7%), classifying donor return behavior, and optimizing appointment scheduling (reducing no-shows by 23%). The platform also includes real-time dashboards and automated reporting tools. The findings show that AI-driven platforms can significantly enhance operational efficiency, reduce blood wastage, and ensure better preparedness in resource-constrained environ ments