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

Area of Science:

• Nephrology and Artificial Intelligence
• Medical Informatics
• Predictive Analytics in Healthcare

Background:

• Organ transplantation is vital for end-stage organ disease.
• Accurate transplant need prediction improves patient outcomes and resource management.
• Kidney transplant prioritization faces challenges due to organ scarcity and complex decision-making.

Purpose of the Study:

• To develop an AI-driven framework for predicting kidney transplant necessity.
• To enhance early detection and prioritization of organ transplant candidates.
• To improve clinical decision-making and resource allocation in transplantation.

Main Methods:

• An AI predictive framework integrating patient demographics, clinical data, and organ status.
• Analysis of 1000 kidney transplant records using ensemble learning (random forest, gradient boosting) and deep neural networks.
• Binary classification model to predict real-world transplant outcomes.

Main Results:

• Deep neural networks achieved superior performance over ensemble models.
• The deep learning model demonstrated high accuracy, precision, recall, and F1-score (AUC 0.95).
• Sensitivity analysis confirmed a favorable balance between high sensitivity and specificity for clinical application.

Conclusions:

• The AI approach shows significant promise for identifying organ transplant candidates.
• This system aids medical professionals in complex risk evaluations for transplantation.
• The findings support the use of AI in optimizing organ transplant candidate selection.