Organ Tissue Transplant Prediction

Abstract

This project focuses on developing an intelligent prediction model for organ tissue transplant compatibility using advanced machine learning and data-driven decision support techniques. The system integrates patient medical records, genetic information such as Human Leukocyte Antigen (HLA) typing, blood group, and biochemical parameters to predict the donor–recipient matching probability. By analysing historical transplant data and learning complex relationships between genetic markers and immune responses, the proposed model aims to minimize the risk of graft rejection and improve clinical decision-making efficiency.

The model employs supervised learning algorithms like Random Forest, Support Vector Machine (SVM), and Neural Networks to classify and predict compatibility levels. A feature selection mechanism ensures that only the most influential medical parameters are considered, enhancing accuracy and reducing computational complexity. Additionally, the system may use optimization techniques to prioritize the best donor-recipient pairs when multiple candidates are available.