Explainable AI Revolutionizing Kidney Transplants: Seeing Clearly to Save Lives

Due to the inadequacies of sagacious matching systems, it has been established that there is a gross scarcity of donor kidneys; hence patients wait for a very long time. Because the medical data is so complicated and encompasses genetic compatibility, health history, and comorbidities, among others, finding the suitable match is difficult. In this paper, we propose an Explainable AI (XAI) model which predicts graft survival rates and complication risks using the SHAP (SHapley Additive exPlanations) technique. The intention is to create a transparent model that enables doctors to merge data insights with their clinical experience. Through improvement of the donor-recipient matching processes, XAI thereby solves the problem of a deficit in organs and thus guarantees favorable outcomes for the patient since the Mean Squared Error is 0.089 for graft survival and 0.085 for complications. problem of organ deficit and promotes positive patient outcomes. Incorporating XAI into clinical decision-making processes does not only help speed up these decisions but also increase confidence among patients and physicians. The objective of this revolutionary approach is to alter kidney transplant surgery into a more effective, efficient and patient-centered practice as a way of combating the problem regarding organ scarcity.