Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia

Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia

Abstract Introduction The prevalence of end-stage renal disease has raised the need for renal replacement therapy over recent decades. Even though a kidney transplant offers an improved quality of life and lower cost of care than dialysis, graft failure is possible after transplantation. Hence, this...

Full description

Bibliographic Details
Main Authors: Getahun Mulugeta, Temesgen Zewotir, Awoke Seyoum Tegegne, Leja Hamza Juhar, Mahteme Bekele Muleta
Format: Article
Language:English
Published: BMC 2023-05-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Renal transplantation
Graft failure
Imbalanced Data
Tree-based ensembles
Stacking ensemble
Probabilistic models
Online Access:https://doi.org/10.1186/s12911-023-02185-5

Internet

https://doi.org/10.1186/s12911-023-02185-5

Similar Items