Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study.

ABSTRACT:

Background

Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation.

Objective

The aim of this study was to predict the risk of kidney graft failure across three temporal cohorts (within 1 year, within 5 years, and after 5 years following a transplant) based on donor and recipient characteristics. We analyzed a large data set comprising over 50,000 kidney transplants covering an approximate 20-year period.

Methods

We applied machine learning-based classification algorithms to develop prediction models for the risk of graft failure for three d

SUBMITTER: Naqvi SAA 

PROVIDER: S-EPMC8433864 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature