Cloud-Based Blood Banking with Real-Time Donor Tracking Using Machine Learning

DOI

10.2991/978-94-6463-866-0_39How to use a DOI?

Keywords

Blood bank management; cloud computing; machine learning; mobile technology; donor tracking; resource allocation

Abstract

Blood banks play a crucial role in ensuring safe and timely blood transfusions, making them essential to healthcare systems. However, traditional blood bank management often relies on manual processes, leading to inefficiencies, delays, and logistical challenges, especially during emergencies. This paper presents a cloud-based blood bank management system designed to enhance the efficiency, accessibility, and security of blood donation and distribution. Leveraging cloud computing, mobile technology, and machine learning, the system optimizes resource allocation, streamlines donor registration, enables real-time tracking of donors and blood units, and offers predictive analytics for inventory management and demand forecasting. An intuitive Android application ensures seamless coordination among blood banks, hospitals, donors, and recipients while prioritizing user privacy, data security, and safe transactions through advanced encryption and multi-factor authentication. The system features an AI-powered recommendation engine that matches donors with recipients based on blood type compatibility, location, and urgency, reducing response times and minimizing blood wastage. Automated alerts and reminders encourage repeat donations, helping to maintain a stable blood supply. Additional functionalities include geolocation services to guide donors to the nearest donation centers and real-time updates on blood stock levels to prevent shortages or overstocking. Integration with wearable health devices and electronic health records (EHRs) enhances donor health monitoring, ensuring that only healthy individuals participate. By replacing paper-based workflows with a secure, intelligent platform, the system significantly improves operational efficiency, minimizes errors, strengthens emergency preparedness, and ultimately leads to better patient outcomes and healthcare delivery.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Aman Raj
AU  - Ankit Singh
AU  - Md Mozammil Ashraf
AU  - Khan Samim
AU  - Danthuluri Sudha
AU  - Junaid Mundichipparakkal
PY  - 2025
DA  - 2025/10/31
TI  - Cloud-Based Blood Banking with Real-Time Donor Tracking Using Machine Learning
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 461
EP  - 471
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_39
DO  - 10.2991/978-94-6463-866-0_39
ID  - Raj2025
ER  -