A Hybrid Deep Learning and Multi-Criteria Optimization Framework for Intelligent and Accurate Kidney Donor–Recipient Matching

Kidney donation and matching of organ donors with the recipients is a life-sensitive exercise that requires a high level of precision and multifaceted assessment. This paper proposes a new model that integrates deep learning structures with multi-criteria optimization to improve the precision of organ allocation. The main point is to overcome simple blood type and tissue matching by adding additional variables, such as physiological, genetic, and logistical factors. In the study, a mixed dataset of four hundred and ninety-eight distinct cases of donor-receivers is used. To analyze this data and build predictive models, computational tools such as Python-based neural network libraries and powerful optimization solvers were used. The deep learning part explains complex, non-linear trends in historical graft survival data. In contrast, the optimization part ensures that the final similar choices are made in accordance with ethical considerations and clinical urgency. Findings show that the hybrid model is much better than traditional scoring systems at predicting long-term graft success and waitlist mortality. This framework provides an open-source, scalable tool for medical professionals to make decisions based on real-time data.