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

Sangeetha Gnanasambandan¹, Vanathi Balasubramanian²

¹Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Chennai, Tamil Nadu, India. sangeethagnanasambandan.mail@gmail.com.

Journal of Artificial Organs : the Official Journal of the Japanese Society for Artificial Organs

|June 6, 2025

Summary

This study introduces a data-driven framework to improve organ transplantation efficiency using advanced algorithms for risk assessment, donor-recipient matching, and allocation. The system significantly enhances accuracy, efficiency, and fairness in organ distribution, improving patient outcomes.

Keywords:

Donor–recipient matching and allocation Organ transplantation Prioritization Risk assessment

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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.