Kidney transplantation stands as the most effective treatment for end-stage kidney disease, yet the success of such procedures relies heavily on post-transplantation care and monitoring. This paper presents a comprehensive framework for enhancing post-transplantation care using a combination of artificial intelligence (AI), machine learning (ML), and blockchain technologies. The proposed system incorporates two novel modules: the Kidney Evaluation Module and the Cox Model Prediction, alongside traditional post-transplant monitoring. The Kidney Evaluation Module utilizes deep learning techniques to assess kidney health from medical images, employing data augmentation, SMOTE for class balancing, and a Deep Feature Fusion Network (DFFN) to categorize kidney conditions accurately. The Cox Model Prediction module employs Cox linear regression to predict patients' waiting times for transplantation based on various predictors such as age, gender, dialysis duration, and cPRA, offering personalized estimations to aid informed decision-making for patients and physicians. Leveraging ensemble methods and custom neural networks, the system predicts early kidney rejection and categorizes patients based on depression severity using the Hamiltonian Depression Scale. Furthermore, blockchain based smart contracts ensure secure storage and traceability of transplant records, while appointment scheduling and a chatbot utilizing TF-IDF address post-transplant queries and provide timely support. Through these integrated modules, the project endeavours to revolutionize kidney transplantation care, offering comprehensive support to patients and facilitating improved clinical outcomes.

Kidney transplantation post-transplantation care Artificial intelligence (AI) Machine learning (ML) Blockchain technologies Ensemble methods Hamiltonian Depression Scale Cox Regression