Corresponding Author: Divyashree Duggegowda
Precise and coherent transplant predictions are hampered by numerous concerns with recent electronic Health Records medical institutions, involving data crumbling, privacy hazards, and unpredictable data features. This study develop a unique paradigm XAI-BFL model that connects Explainable Artificial Intelligence (XAI), authorization blockchain, and federated learning techniques to forecast transplant decisions utilizing distributed electronic health information. The envisioned framework is judged by using standard category metrics and ROC curve testing to measure predictive performance. Result achieved outcomes determined substantial improvements, achieving 93.8% accuracy, 93.1% precision and 92.6% recall. Comparative assessment against baseline Artificial Intelligence models, standalone AI, and federated learning advances exhibited that the recommended XAI-BFL model transfers superior organ-transplantation prediction effectiveness. The solutions point to the recommended framework as a privacy-preserving, and coherent medical decision-support tool.