Predictive models for posttransplant diabetes mellitus in kidney transplant recipients using machine learning and deep learning approach: a nationwide cohort study from South Korea.

ABSTRACT:

Background

Posttransplant diabetes mellitus (PTDM) complicates kidney transplant recipients (KTRs) in morbidity and mortality. This study aimed to predict PTDM risk in KTRs using machine learning and deep learning models.

Methods

Data were obtained from the Korea Organ Transplantation Registry, a nationwide cohort study of KTRs. Four machine learning algorithms, including eXtreme Gradient Boosting (XGBoost), CatBoost, light gradient boosting machine and logistic regression, and deep learning were implemented on 41 pretransplant and 31 posttransplant variables to predict PTDM. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic curve, accuracy, precision, recall, and F1 score.

Results

Among 3,213 KTRs, 497 patien

SUBMITTER: Choi S 

PROVIDER: S-EPMC12611607 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature