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

The Stochastic Gradient Boosting model demonstrated the highest predictive performance, while the Ridge-Cox model offered better interpretability with a comparable performance. Clinicians should consider the trade-off between prediction accuracy and interpretability when selecting a model. Incorporating these findings into the clinical practice can improve risk stratification and personalized management strategies for kidney transplant recipients.