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Dr. Mahalingam College of Engineering & Technology Department of Artificial Intelligence & Data Science 23ADP501 – Reverse Engineering Project Zeroth Review.
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Problem Statement Abstract Literature Review and Findings Case study in the problem domain identified Tools Used Conclusion References.
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In critical medical situations such as road accidents, emergency surgeries, or sudden organ failures, finding a compatible blood or organ donor immediately is often the key to saving a life. However, current donor search methods are slow and inconsistent. Static databases, manual communication, and delayed information exchange lead to the loss of valuable time. The lack of integration of real-time location data, medical compatibility details, and donor availability makes accurate matching and quick access difficult. As a result, many opportunities to save lives during emergencies are missed, leaving patients vulnerable to preventable delays in receiving critical medical support..
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This project develops an AI-powered mobile and web platform for real-time blood and organ donor identification and emergency communication. Donors register with blood group, organ donation preferences, and location. In emergencies, AI algorithms match requests with nearby eligible donors based on location, compatibility, and predicted availability. Verified hospitals and relatives get controlled access to donor contact details to reduce response delays. The platform also enables posting and registration for blood donation camps, removing the need for traditional publicity. Machine learning improves matching accuracy, predicts donor availability, and forecasts high-demand blood types, with strong emphasis on privacy, security, and consent-based data sharing..
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Literature Identified and Findings.
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Blood Transfusion Optimization & Shortage Prediction.
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Matching Algorithms for Blood Donation. Reference Paper D. C. McElfresh et al., “Matching Algorithms for Blood Donation,” arXiv, Aug. 2021..
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Blood Donation Drive Management. Reference Paper S. S. Singh, D. Gupta, and V. Anand, “Blood Donation Drive Management: A Discrete Event Simulation-Based Approach for Enhanced Operational Efficiency,” J. Blood Serv. Econ., ahead-of-print, 2024...
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Smart Blood Donor Locator. Problem: Delay in locating suitable blood donors during disasters. Algorithm: GPS-based donor filtering with component-level matching and disaster-triggered notifications. Gap: Lacks AI-based prioritization and donor availability prediction; relies on basic filtering. Result: Enables rapid identification of nearby donors and automates notification workflows..
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AI-Based Donor Eligibility Prediction. Reference Paper V. Shelake et al., “Enhancing Donor Eligibility Criteria using Machine Learning to Maximize Blood Donation Efficiency,” J. Inf. Syst. Eng. Manag., vol. 10, no. 27s, 2025..
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AI-Powered Donor Behaviour Prediction. Reference Paper T. T. Aung et al., “A Comparative Analysis of Machine Learning Models in Predicting Blood Donation Behavior,” Int. J. Res. Sci. Innov., June 2025..
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Case study. Friends2Support Connects donors & seekers via mobile & web. Large user base but no AI-based matching, no organ donation support, and no relative direct access. Our project reverse-engineers this model and extends it with: AI availability prediction Blood camp management Verified emergency caller access.
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Tools Used. Frontend: ReactJS Backend: Node.js + Express, Firebase Cloud Functions Database: Firebase AI/ML: Python, scikit-learn, TensorFlow Lite APIs: Google Maps API, Twilio SMS, Firebase Cloud Messaging Version Control: GitHub Design: Figma, Canva Hosting: Firebase Hosting, Vercel.
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Conclusion. The proposed platform addresses the critical need for real-time, AI-powered donor identification in medical emergencies. By integrating location-based matching, donor availability prediction, relative/hospital direct access, and donation camp management, the solution enhances response speed, improves matching accuracy, and increases public participation. Reverse-engineering of existing donor platforms allows leveraging proven features while innovating with AI, predictive analytics, and privacy-first design..
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THANK YOU.