A data-driven framework for fair and efficient organ transplantation using gradient boosting and adaptive genetic...

Area of Science:

• Medical Informatics
• Biomedical Engineering
• Computational Biology

Background:

• Organ transplantation faces challenges in efficiency, risk assessment, donor-recipient matching, and equitable allocation.
• Current systems often struggle with long waiting times and suboptimal patient outcomes due to allocation inefficiencies.

Purpose of the Study:

• To develop and evaluate a comprehensive data-driven framework to optimize organ transplantation processes.
• To enhance efficiency, accuracy, and fairness in organ allocation and distribution.

Main Methods:

• Utilized gradient boosting algorithm (GBA) for risk prioritization.
• Employed A* search for optimal donor location.
• Implemented a modified convolutional neural network-based hybrid extreme learning classifier (MCNN-HELM) for precise matching.
• Developed an adaptive objective-weighted genetic allocation (AOWGA) algorithm for equitable distribution.

Main Results:

• The integrated framework achieved 96% overall accuracy and 97% allocation efficiency.
• The MCNN-HELM model demonstrated 94% matching precision and 97.5% accuracy.
• AOWGA algorithm showed 0.96 allocation efficiency and 0.95 positive outcome rate.
• The system achieved a fairness index of 0.92.

Conclusions:

• The proposed framework significantly improves organ allocation processes, reduces waiting times, and enhances patient survival rates.
• The integration of GBA, A* search, MCNN-HELM, and AOWGA sets a new standard for ethical and efficient organ transplantation.
• This data-driven approach addresses organ shortage and promotes equitable distribution, leading to better patient outcomes.