Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models
Getahun Mulugeta,
Temesgen Zewotir,
Awoke Seyoum Tegegne
et al.
Abstract:Introduction:
Renal transplantation is a critical treatment that can save the lives of individuals who are suffering from end-stage renal disease (ESRD), but graft failure remains a significant concern. Accurate prediction of graft survival after renal transplantation is crucial as it enables clinicians to identify patients at higher risk of graft failure. This study aimed to develop clinical prognostic models for predicting graft survival after renal transplantation and compare the performance of various sta… Show more
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