Prediction of graft outcomes after kidney transplantation: When standard statistics compare to machine learning techniques

While most ML models outperformed LR, the differences were not substantial. This may be attributed to the small dataset size, which likely contributed to the overall poor performance. We recommend using these complex models with high-quality datasets that include a sufficient number of variables and observations to fully leverage their potential. The key question for future research is determining the dataset size required for ML to become the primary analytic tool for predicting kidney transplant outcomes.