Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models

Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models

Abstract Introduction Renal transplantation is a critical treatment for end-stage renal disease, but graft failure remains a significant concern. Accurate prediction of graft survival is crucial to identify high-risk patients. This study aimed to develop prognostic models for predicting renal graft...

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Bibliographic Details
Main Authors: Getahun Mulugeta, Temesgen Zewotir, Awoke Seyoum Tegegne, Mahteme Bekele Muleta, Leja Hamza Juhar
Format: Article
Language:English
Published: BMC 2025-02-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Renal transplant
Graft survival
SMOT oversampling
Prognostic models
Statistical models
Machine learning models
Online Access:https://doi.org/10.1186/s12911-025-02906-y
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https://doi.org/10.1186/s12911-025-02906-y

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