A systematic review of data mining applications in kidney transplantation

Aslani, Nasim and Galehdar, Nasrin and Garavand, Ali (2023) A systematic review of data mining applications in kidney transplantation. Informatics in Medicine Unlocked.

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Abstract

Introduction Kidney transplantation is one of the most important surgical operations requiring much attention to patients' condition after the transplant. Data mining can be used as a useful tool to inspect the condition of these patients and provide reliable advice to physicians. The aim of this study was to identify the data mining applications in kidney transplantation. Methods This systematic review was conducted in 2022. We searched PubMed, Web of Science, and Embase scientific databases, based on the combination of related MeSH terms. The articles selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles selection was done by using the inclusion and exclusion criteria. Data gathering was done using a data extraction form. Results Of the 873 articles finally 31 articles were selected based on the inclusion and exclusion criteria. The results show that 85% of the algorithms are applied to the prediction of kidney transplantation success or failure. Decision Tree (nine studies) and Bayesian (eight studies) techniques were the most frequent techniques among the prevalent machine learning algorithms. Conclusion The use of data mining methods can play a functional role in predicting the results of kidney transplantation. It is recommended to use the data mining method to help predict the best method of diagnosis and treatment in kidney transplants.

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