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Performance Evaluation of Enhanced Artificial Neural Network for Kidney Donor Recipient Matching
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Author(s): P. Kiruthiga (Bharath Institute of Higher Education and Research, Chennai, India)and S. Silvia Priscila (Bharath Institute of Higher Education and Research, Chennai, India)
Copyright: 2026
Pages: 28
Source title:
AI in Health and Human-Centric Systems
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Muthmainnah (Universitas Al Asyariah Mandar, Indonesia), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)and Michael Baron (Analytics Institute of Australia, Australia)
DOI: 10.4018/979-8-3373-6796-5.ch004
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Abstract
Kidney donor-recipient matching operations need precise execution to guarantee transplant achievements. The accuracy rate of Random Forest (RF) and Naïve Bayes (NB) at 85–88%, along with slow processing times, causes mismatches that result in higher rejection rates. An Enhanced Artificial Neural Network (EANN) includes game-theoretic feature selection technology, quantum-inspired optimization functions, and sparse tensor computation to enhance donor-recipient compatibility prediction performance. The experimental model operated on transplant data containing information about blood types, HLA matches, and medical documentation. Experimental outcomes revealed that EANN surpassed other models by delivering an accuracy level of 92.0%, while CNN reached 90.5%, Naïve Bayes achieved 88.1%, and Random Forest's 86.2%. The processing time decreased to 14.8 ms with a simultaneous reduction of false match rates to 2.1%. Transplant allocation depends on blockchain technology to establish the model's transparent management.
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