Comparative Evaluation of Machine Learning Models for Predicting Donor Kidney Discard

TL;DR

This study demonstrates that consistent data preprocessing, feature selection, and evaluation can be more decisive for predictive success than the choice of the ML algorithm.

Abstract

A kidney transplant can improve the life expectancy and quality of life of patients with end-stage renal failure. Even more patients could be helped with a transplant if the rate of kidneys that are discarded and not transplanted could be reduced. Machine learning (ML) can support decision-making in this context by early identification of donor organs at high risk of discard, for instance to enable timely interventions to improve organ utilization such as rescue allocation. Although various ML models have been applied, their results are difficult to compare due to heterogenous datasets and differences in feature engineering and evaluation strategies. This study aims to provide a systematic and reproducible comparison of ML models for donor kidney discard prediction. We trained five commonly used ML models: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Deep Learning along with an ensemble model on data from 4,080 deceased donors (death determined by neurologic criteria) in Germany. A unified benchmarking framework was implemented, including standardized feature engineering and selection, and Bayesian hyperparameter optimization. Model performance was assessed for discrimination (MCC, AUC, F1), calibration (Brier score), and explainability (SHAP). The ensemble achieved the highest discrimination performance (MCC=0.76, AUC=0.87, F1=0.90), while individual models such as Logistic Regression, Random Forest, and Deep Learning performed comparably and better than Decision Trees. Platt scaling improved calibration for tree-and neural network-based models. SHAP consistently identified donor age and renal markers as dominant predictors across models, reflecting clinical plausibility. This study demonstrates that consistent data preprocessing, feature selection, and evaluation can be more decisive for predictive success than the choice of the ML algorithm.

Figures (17)

• Figure 1: Schema of the feature selection process. Starting from the full feature space, multiple candidate feature subsets are created and evaluated by training and validating a supervised model. Each subset is assigned a loss value based on its predictive performance, and the subset with the smallest loss is selected as the final feature subset.
• Figure 2: Left: Discrimination performance evaluation of six different ML models. Each panel reports the normed test MCC (Y-axis, higher is better) for multiple approaches: XDT, LR, RF, XGB, MLP, and DE. Boxplots show the performance distribution for each method. Right: Lower-triangle heatmap of Tukey-adjusted p-values showing pairwise statistical significance between models.
• Figure 3: Calibration curves without post-calibration for DT, RF, LR, XGB, and MLP.
• Figure 4: Calibration curves for platt post-calibration for DT, RF, LR, XGB, and MLP.
• Figure 5: Beeswarm plot of SHAP values showing the distribution and impact of features on LR donor transplantation predictions. Each point represents an individual instance (test set), positioned by its SHAP value and colored by the feature’s actual value, illustrating both the magnitude and direction of feature influence. Top features include age, time-series aggregates of renal function measures and medication information.