Predictive Modeling of Organ Transplantation Using Ensemble Learning and Deep Neural Networks.

OBJECTIVES: Organ transplantation functions as a critical medical procedure that serves patients with severe multiorgan conditions and chronic diseases. Early and accurate predictions of requirements for transplant enhance patient care through better clinical choices and improved resource allocation and treatment selection. We developed a complex artificial intelligence forecasting system to determine organ transplant needs, specifically kidney diseases by analyzing clinical data. Accurate and timely prioritization of patients for kidney transplant is a critical challenge due to organ scarcity and the complexity of clinical decision -making.

MATERIALS AND METHODS: In this study, we propose an artificial intelligence -driven predictive framework to assess kidney transplant necessity by integrating patient demographics, clinical severity indicators, donor matching status, and real -time organ condition data. We analyzed a structured dataset consisting of 1000 kidney transplant records with 25 attributes by using ensemble learning models, including random forest and gradient boosting, and a deep neural network model. The prediction task was formulated as a binary classification problem reflecting real -world transplant outcomes.

RESULTS: Experimental results demonstrated that the deep neural network consistently outperformed ensemble -based models, achieving superior accuracy, precision, recall, and F1 -score with an area under the receiver operator characteristic curve of 0.95. Threshold -based sensitivity analysis confirmed that the deep learning model provided a clinically favorable trade -off between high sensitivity and acceptable specificity.

CONCLUSIONS: The proposed approach shows promising results in helping medical professionals detect organ transplant candidates through complex risk evaluation assessments.