Abstract
Background/objectives: Patients of kidney failure sometimes have incompatible donors. This study proposes an application to get the best matchings based on scoring data. Methods: For pairwise matching, we created a new graph from the original scoring matrix. This graph ensures pairwise matchings. To find an optimal matching, we used the Blossom algorithm. For multiway matching, we interpreted the scoring matrix as an assignment problem. For this, we used the Hungarian algorithm. The application was created using Python, NetworkX, NumPy, and PySimpleGUI. The app uses CSV files as input. Findings: Both algorithms made for polynomial run times. Matching is fast and is guaranteed to be optimal. The application itself gets instantaneous results even for large donor-patient matrices. Application/improvement: Hospitals or organizations can use this application for their kidney matching programs. Keywords: Blossom Algorithm, Hungarian Algorithm, Kidney Failure, Donor Matching.
Highlights
Chronic kidney disease is the gradual loss of kidney function
This can progress to a state called end-stage renal disease (ESRD)
Kidney failure is usually permanent. Those who have ESRD can choose from two treatment options: dialysis or transplantation
Summary
Chronic kidney disease is the gradual loss of kidney function. This can progress to a state called end-stage renal disease (ESRD). ESRD patients will need dialysis for the rest of their lives. Incompatibilities may be immunological or due to blood type To address this problem, exchange programs were created. The higher the compatibility, or matching score, the higher the success of an exchange. This would give us an idea if a patient’s body will accept the donated kidney. With a pool of matching scores (Figure 1), we could find an optimal matching This would give us the best donor-patient exchanges. We provide two proven solutions adapted for pairwise and multiway matching
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