Ranking patients on the kidney transplant waiting list based on fuzzy inference system

Nasrin Taherkhani,Shadi Shafaghi,Mohammad Mehdi Sepehri,Roghaye Khasha

doi:10.1186/s12882-022-02662-5

Abstract

BackgroundKidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed to rank the patients based on kidney allocation factors. The main objective was to develop an expert system, which would mimic the expert intuitive thinking and decision-making process in the face of the complexity of kidney allocation.MethodsIn the first stage, kidney allocation factors were identified. Next, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to weigh them. The purpose of this stage is to develop a point scoring system for kidney allocation. Fuzzy if-then rules were extracted from the United Network for Organ Sharing (UNOS) dataset by constructing the decision tree, in the second stage. Then, a Multi-Input Single-Output (MISO) Mamdani fuzzy inference system was developed for ranking the patients on the waiting list.ResultsTo evaluate the performance of the developed Fuzzy Inference System for Kidney Allocation (FISKA), it was compared with a point scoring system and a filtering system as two common approaches for kidney allocation. The results indicated that FISKA is more acceptable to the experts than the mentioned common methods.ConclusionGiven the scarcity of donated kidneys and the importance of optimal use of existing kidneys, FISKA can be very useful for improving kidney allocation systems. Countries that decide to change or improve the kidney allocation system can simply use the proposed model. Furthermore, this model is applicable to other organs, including lung, liver, and heart.

Highlights

Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD)
To determine the inputs of the system, kidney allocation factors were identified from the literature and were verified by Iranian experts
Given that patients ranking can be difficult for experts, especially when faced with a huge number of choices [9], we considered a small dataset to allow the experts to prioritize patients more and accurately

Summary

Introduction

Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). The kidney was allocated to the patient with the highest number of total points [9, 10]. In the United Network for Organ Sharing (UNOS) kidney allocation system, patients under years old receive four points [4], but a years old patient receives zero points. Such a sharp drop in the number of received points is not justifiable. Determining a crisp boundary for the patient’s age leads to this injustice It seems that the scoring method cannot mimic the expert decision-making process

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Discussion

Conclusion