Intelligent Organ Transplantation Channel Using Machine Learning

Abstract: Efficient donor–recipient matching is a critical step in organ transplantation, yet most hospitals still depend on manual comparison of clinical factors such as age, blood group, comorbidities, and organ-specific health indicators. This manual process is slow, prone to inconsistency, and unsuitable for handling the rapid inflow of medical data in real-world environments. To overcome these challenges, this study introduces an intelligent, machine-learning–enabled matching system designed to provide fast, reliable, and data-driven compatibility predictions. The proposed web-based framework incorporates Random Forest and K-Nearest Neighbours models along with computed clinical metrics—including a compatibility score, an organ function score, and a consolidated match score—to evaluate donor–recipient pairs for heart, kidney, liver, and lung transplants. The platform integrates role-based interfaces for administrators, doctors, receptionists, and patients, ensuring streamlined data entry, treatment management, and prediction access. Experimental analysis shows that the system delivers accurate compatibility assessments with efficient real-time execution, demonstrating the potential of machine learning to minimize mismatches, shorten waiting periods, and enhance clinical decision support in transplant workflows. The modular architecture also supports future expansion to additional organs and evolving hospital datasets.

Keywords: Organ Transplantation, Donor–Recipient Matching, Machine Learning, Random Forest Classifier, K-Nearest Neighbours (KNN), Compatibility Prediction, Clinical Decision Support System, Medical Data Processing.