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

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CONCLUSIONS: 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.

NCBI     Feb 03, 2025