Published Paper Details:

Organ transplantation remains one of the most significant medical procedures for restoring the quality of life of patients suffering from organ failure and severe vision impairment. Despite advancements in transplantation medicine, the success of organ transplantation is often limited by the availability of compatible donors and the complexity of donor-recipient matching. Traditional methods primarily rely on manual assessment and basic compatibility analysis, which may not always provide optimal transplantation outcomes. This research proposes a data-driven prediction system for organ transplant viability and recovery using Machine Learning techniques. The proposed system utilizes donor and recipient medical information, including demographic details, physiological characteristics, medical history, and transplantation parameters, to predict compatibility and estimate transplantation success. Advanced predictive analytics techniques are employed to identify suitable donor-recipient pairs and reduce the likelihood of transplant rejection. The system integrates machine learning algorithms capable of analyzing large volumes of healthcare data to generate accurate predictions regarding transplant viability. The developed framework assists healthcare professionals in making informed decisions while improving efficiency and reducing manual effort. Experimental analysis demonstrates that predictive modeling can significantly improve donor matching accuracy and contribute to enhanced patient recovery outcomes. The proposed approach represents an important step toward intelligent healthcare systems by combining artificial intelligence with transplantation medicine. The results indicate that machine learning-based prediction systems can play a critical role in improving transplantation success rates, minimizing complications, and supporting evidence-based medical decision-making.