Optimal organ allocation with minimized waiting time for transplantation using modified convolutional neural network

Abstract

Organ transplantation is a critical medical intervention for individuals with terminal organ failure, offering a lifeline for those in dire need. However, the persistent shortage of available organs poses a substantial challenge, leading to prolonged waiting times and, tragically, preventable patient deaths. To address this, we leverage the transformative power of big data analytics within the healthcare sector, paving the way for informed decision-making. To make optimal decisions in organ transplantation, initial work presented a modified convolutional neural network-hybrid extreme learning machine (MCNN-HELM) based prediction model. The MCNN-HELM model utilizes three different real-time datasets as inputs which contain records of liver, heart, and lung transplantation details of the donor and recipient. At first, the missing values and inaccurate data present in real-time datasets are removed via pre-processing. The pre-processed data are then trained using the MCNN-HELM model that efficiently determines the suitable donor for the recipient by minimizing the waiting time of the recipient for the matching organ donor. Moreover, the MCNN-HELM model gives initial preference to patients with high-risk rates to improve their quality of life. In second work, a multifaceted approach to enhance organ transplantation, addressing the dual objectives of minimizing waiting times for recipients and prioritizing patients with high medical risk to maximize positive outcomes. The proposed system integrates four key components namely risk assessment, geographic analysis, MCNN-HELM model, and a novel Genetic Algorithm-based optimal allocation strategy. In the risk assessment phase, patients are categorized based on their medical risk levels, enabling prioritization. Geographic analysis leverages A* algorithm-based routing to identify the geographically shortest paths for donor identification, streamlining the transplantation process. The MCNN-HELM model refines donor-recipient matching by harnessing deep learning techniques and enhancing the accuracy of matching predictions. newline