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Orgdan An Integrated GWO Optimized AI Powered Model for Liver Health Assessment and Intelligent Organ Donor Matching
Authors
1 3 4 Department of Computer Engineering, Sardar Patel Institute of Technology, Mumbai, India 2 Assistant Professor, Department of Computer Engineering, Sardar Patel Institute of Technology, Mumbai, India.
Article Information
- Published In
- Volume 6, Issue 6 (2025)
- Published Online
- November-December 2025
- Pages
- 151-158
- Article Type
- Original Article
- Check for updates
-
References
1. Saha B, Adhikari N, Nath S, Mal U, Deyasin S, Ghosh N. Ant Colony Optimization: A Review. Journal of Technology. 2024;12(11):971–987.
2. Rajput AV, Choudhari N. Organ Donation and Transplantation Management System Using Blockchain Technology. Journal of Technology. 2025;13(7):290– 294.
3. Mishra D, Bhatt A, Saluja MS. Conceptual Framework on Machine Learning Algorithms in Healthcare. Journal of Technology. 2024;12(4):683–694.
4. Mishra D, Bhatt A, Saluja MS. Learning Based Optimization for Brain Tumor Segmentation in MR Images. Journal of Technology. 2024;12(4):695–706.
5. Mayo Clinic. Liver function tests. Mayo Clinic. 2025. Available at: https://www. mayoclinic.org/tests-procedures/liver-function-tests/about/ pac-20394595.
6. Asrani SK, et al. Burden of liver diseases in the world. J Hepatol. 2019;70(1):151–171.
7. Wiesner RH, et al. Model for end-stage liver disease (MELD) and allocation of donor livers. Gastroenterology. 2003;124(1):91–96.
8. Kim WR, et al. Hyponatremia and mortality among patients on the liver-transplant waiting list. N Engl J Med. 2008;359(10):1018–1026.
9. Bertsimas D, et al. Interpretable optimal trees. Machine Learning. 2017;106:1039–1082.
10. Lau L, et al. Machine-learning algorithms to predict risk of graft failure after liver transplantation. Transplantation. 2017;101(9):e247–e252.
11. Ronneberger O, et al. U-Net: Convolutional networks for biomedical image segmentation. In: MICCAI. 2015. p. 234–241.
12. Mirjalili S, et al. Grey Wolf Optimizer. Advances in Engineering Software. 2014;69:46–61.
13. Bhat M, Rabindranath M, Simonetto DA. Artificial intelligence and machine learning in liver transplantation. J Hepatol. 2023;78(3):512–528.
14. Chongo G, et al. Machine learning for prognosis in liver transplantation: a systematic review. Transplant Rev. 2024;38(1):100–118.
2. Rajput AV, Choudhari N. Organ Donation and Transplantation Management System Using Blockchain Technology. Journal of Technology. 2025;13(7):290– 294.
3. Mishra D, Bhatt A, Saluja MS. Conceptual Framework on Machine Learning Algorithms in Healthcare. Journal of Technology. 2024;12(4):683–694.
4. Mishra D, Bhatt A, Saluja MS. Learning Based Optimization for Brain Tumor Segmentation in MR Images. Journal of Technology. 2024;12(4):695–706.
5. Mayo Clinic. Liver function tests. Mayo Clinic. 2025. Available at: https://www. mayoclinic.org/tests-procedures/liver-function-tests/about/ pac-20394595.
6. Asrani SK, et al. Burden of liver diseases in the world. J Hepatol. 2019;70(1):151–171.
7. Wiesner RH, et al. Model for end-stage liver disease (MELD) and allocation of donor livers. Gastroenterology. 2003;124(1):91–96.
8. Kim WR, et al. Hyponatremia and mortality among patients on the liver-transplant waiting list. N Engl J Med. 2008;359(10):1018–1026.
9. Bertsimas D, et al. Interpretable optimal trees. Machine Learning. 2017;106:1039–1082.
10. Lau L, et al. Machine-learning algorithms to predict risk of graft failure after liver transplantation. Transplantation. 2017;101(9):e247–e252.
11. Ronneberger O, et al. U-Net: Convolutional networks for biomedical image segmentation. In: MICCAI. 2015. p. 234–241.
12. Mirjalili S, et al. Grey Wolf Optimizer. Advances in Engineering Software. 2014;69:46–61.
13. Bhat M, Rabindranath M, Simonetto DA. Artificial intelligence and machine learning in liver transplantation. J Hepatol. 2023;78(3):512–528.
14. Chongo G, et al. Machine learning for prognosis in liver transplantation: a systematic review. Transplant Rev. 2024;38(1):100–118.
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