In In emergency medical cases, it is essential to access blood donors without delay. Traditional systems struggle with real-time matching, leading to critical delays. This project introduces a web-based E-Blood Banking platform that integrates big data techniques and real-time SMS alerts to improve donor response speed. Developed with React.js (front-end), Python (backend), and MySQL (database), the system maintains a structured database for hospitals and donors. It enables fast matching based on blood group, proximity, and urgency. When a request is submitted, potential donors are notified instantly via SMS—even in offline conditions—enhancing coordination and reducing wait times during emergencies. The ultimate goal is to deliver a more responsive, real-time blood management system for healthcare
Introduction
Blood donation is critical in emergencies but existing blood bank systems face challenges like poor data sharing, weak security, and inefficient donor-recipient coordination, causing delays in urgent transfusions. To address these issues, this project proposes an advanced E-Blood Banking System that integrates digital technologies for secure, fast, and user-friendly management.
Traditional blood banks rely on manual processes, leading to data inconsistency, lack of real-time access, and limited interoperability with hospitals and emergency services. The new system improves scalability and accuracy through centralized database management, real-time location tracking, and automated emergency alerts.
Key features include:
A Database Management System (DBMS) using B-Tree indexing for efficient data retrieval of donors and blood stock.
A K-Nearest Neighbors (KNN) algorithm for accurate donor-recipient matching based on blood group and proximity.
An Emergency Alert System that sends real-time notifications via SMS and web push, operating even without internet access to ensure quick donor response in emergencies.
The platform promotes interoperability among hospitals, blood banks, and donors with role-based security controls, enhancing data privacy and system reliability. By automating alerts and optimizing donor matching, the system aims to reduce delays in blood transfusion and improve emergency response effectiveness.
Conclusion
The E-Blood Banking System plays a crucial role in bridging the gap between donors and recipients by leveraging location-based donor matching and an emergency alert system. This system significantly reduces the time required to find suitable donors, ensuring that patients in critical need receive blood as quickly as possible. By integrating a user-friendly interface with real-time database updates, the platform enhances accessibility and efficiency in blood donation management. The emergency alert system further ensures that donors are notified promptly, even in offline conditions, improving response rates and saving more lives.
To further enhance the system’s effectiveness, several improvements can be considered in the future:
Block chain Technology: Securely stores donor and recipient data, preventing tampering and ensuring transparency.
Drone-Based Blood Transport: Implementing drone delivery systems to rapidly transport blood units to remote or emergency locations.
Offline Functionality: Developing an enhanced offline mode to allow users to access donor databases and send emergency alerts without an active internet connection.
Multilingual Support: Expanding language options to accommodate users from diverse linguistic backgrounds, making the system more inclusive.
Automated Blood Stock Management: Integrating predictive analytics to help hospitals and blood banks manage supply levels efficiently.
With these advancements, the E-Blood Banking System will continue to evolve, making blood donation more secure, accessible, and efficient, ultimately improving healthcare services and saving more lives.
References
[1] S. Fedushko, O. Trach, Y. Syerov, N. Kryvinska, and J. R. Calhoun, \"Web Project Development: Emergency Management,\" River Publishers Journals & Magazine, IEEE Xplore, 2024. [Online]. Available:https://ieeexplore.ieee.org/document/10247349.(Accessed:27-Mar-2025)
[2] A. Alharbi, S. Alharthi, and M. Alzahrani, \"Progression towards an e-Management Centralized Blood Donation System in Saudi Arabia,\" presented at the 2020 International Conference on Computing and Information Technology (ICCIT-1441), Tabuk, Saudi Arabia, Sep. 2020,pp.15.[Online].Available:https://ieeexplore.ieee.org/document/9194178. [Accessed: 27-Mar-2025].
[3] S. Kim and D. Kim, \"Design of an Innovative Blood Cold Chain Management System Using Block chain Technologies,\" ICIC Express Letters, Part B: Applications, vol. 9, no. 10, pp. 1067–1073, Oct. 2018, DOI: 10.24507/icicelb.09.10.1067.
[4] A. Aminuddin, M. Z. Saringat, S. A. Mostofa, A. Mustapha, and M. H. Hassan, \"A Case Study on B-Tree Database Indexing Technique,\" Journal of Soft Computing and Data Mining, vol. 1, no. 1, pp. 27–35, Mar. 2020.
[5] P. Koruga and M. Ba?a, \"Analysis of B-tree Data Structure and its Usage in Computer Forensics,\" in Proceedings of the 21st Central European Conference on Information and Intelligent Systems (CECIIS 2010), Varaždin, Croatia, Sep. 2010, pp. 423–428. [Online]. Available: https://www.researchgate.net/publication/210381551_Analysis_of_B-tree_data_structure_and_its_usage_in_computer_forensics.[Accessed: 27-Mar-2025]..
[6] M. Suyal and P. Goyal, \"A Review on Analysis of K-Nearest Neighbor Classification Machine Learning Algorithms based on Supervised Learning,\" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 43–48, Jul. 2022, DOI: 10.14445/22315381/IJETT-V70I7P205.
[7] R. K. Halder, M. N. Uddin, M. A. Uddin, S. Aryal, and A. Khraisat, \"Enhancing K-nearest neighbor algorithm: a comprehensive review and performance analysis of modifications,\" Journal of Big Data, vol. 11, no. 1, Article 113, Aug. 2024. [Online]. Available: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-024-00973-y. [Accessed: 28-Mar-2025.
[8] Y. Luo, G. Lu, and Y. Wu, \"Design and Analysis of Blood Donation Model Based on Blockchain and KNN,\" in Proceedings of the 2021 3rd Blockchain and Internet of Things Conference (BIOTC), Ho Chi Minh City, Vietnam, Jul. 2021, pp. 32–37, DOI: 10.1145/3475992.3475997.
[9] Twilio Inc., \"Emergency Calling for SIP,\" Twilio, 2025. [Online]. Available: https://www.twilio.com/docs/voice/sip/emergency-calling.