Predicting prognostic factors in kidney transplantation using a machine learning approach to enhance outcome predictions: a retrospective cohort study.

ABSTRACT:

Background

Accurate forecasting of clinical outcomes after kidney transplantation is essential for improving patient care and increasing the success rates of transplants. The authors' study employs advanced machine learning (ML) algorithms to identify crucial prognostic indicators for kidney transplantation. By analyzing complex datasets with ML models, the authors aim to enhance prediction accuracy and provide valuable insights to support clinical decision-making.

Materials and methods

Analyzing data from 4077 KT patients (June 1990-May 2015) at a single center, this research included 27 features encompassing recipient/donor traits and peri-transplant data. The dataset was divided into training (80%) and testing (20%) sets. Four ML models-eXtreme Gradient Boosting (XGBoost)

SUBMITTER: Kim JM 

PROVIDER: S-EPMC11573070 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature