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

Journal: BMC medical informatics and decision making
PMID: 39901148

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 survival and compare the performance of statistical and machine learning models.

Authors

  • Getahun Mulugeta
    Department of Statistics, Bahir Dar University, Bahir Dar, Ethiopia. gech.marr@gmail.com.
  • Temesgen Zewotir
    School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, 4001, Durban, South Africa.
  • Awoke Seyoum Tegegne
    College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
  • Mahteme Bekele Muleta
    Kidney Transplant Center, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia.
  • Leja Hamza Juhar
    Kidney Transplant Center, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia.

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