AI-Based Organ Health Analysis for Organ Donation Management with Blockchain

Abstract

Organ transplantation represents one of medicine's most critical and complex procedures, with over 150,000 patients awaiting organs globally and a critical shortage of viable donors. The current organ donation ecosystem suffers from fundamental challenges: lack of transparency in organ allocation, fragmented communication between stakeholders, risk of organ damage during transport, post-transplant rejection prediction, and vulnerability to data tampering and trafficking. This paper introduces a comprehensive AI-Blockchain integrated platform for organ donation management that connects four key stakeholders—Donors, Brokers, Hospitals, and Patients—through a Python Flask-based web application. The system addresses the complete organ transplantation lifecycle: donor registration and health profiling, organ harvesting and preservation, matching and allocation, transport tracking, transplant surgery, and post-operative monitoring. A novel contribution is the Organ Health Analysis (OHA) module that employs ensemble machine learning algorithms to predict organ viability and post-transplant success probability. The OHA analyzes 47 clinical parameters including donor demographics, medical history, organ-specific biomarkers, preservation time, ischemic conditions, and recipient compatibility factors. Experimental evaluation on a dataset of 25,000 transplant records demonstrates that the XGBoost-based predictor achieves 94.7% accuracy in predicting 5-year graft survival, with AUC of 0.976 and sensitivity of 0.953. The blockchain infrastructure, implemented using Hyperledger Fabric with 5 organizations and 10 peers, ensures immutable audit trails, transparent allocation, and tamper-proof record keeping with 99.99% data integrity guarantees. Smart contracts automate the matching process based on UNOS criteria, reducing allocation time from days to minutes. The platform processes an average of 1,500 transactions per second with 2.3-second block finality. A role-based access control system ensures that each stakeholder accesses only authorized data: donors control their medical information, brokers manage allocation logistics, hospitals handle clinical data, and patients track waitlist status. The system has been validated through a pilot deployment with three partner hospitals, demonstrating 40% reduction in organ allocation time, 60% improvement in data transparency, and 25% increase in successful transplant matches. This work represents the first integrated AI- blockchain solution for end-to-end organ donation management, addressing critical gaps in transparency,
traceability, and predictive health analysis.