Predicting delayed graft function after kidney transplant: Do complex models help compared to standard statistics?

Table 1 Studies comparing machine learning and logistic regression for predicting delayed graft function

Ref.	Sample size	ML model	AUROC (ML)	AUROC (LR)	Findings
Decruyenaere et al[20], 2015	497	Linear SVM	0.843	0.817	Linear SVM significantly outperformed LR; other ML models showed no significant difference
Kawakita et al[33], 2020	Dev: 55044/Val: 6176	ANN/XGB	0.732/0.735	0.705	ML models had higher AUROC than LR, but the study did not explicitly provide significance testing for those comparisons
Salgado et al[1], 2026	523	GB/RF/XGB/MLP	0.81/0.70/0.62/0.58	0.68/0.62/0.52/0.51	ML models showed modestly higher AUROC/accuracy for some predictor sets, but overall differences between ML and LR were not statistically significant, and both approaches showed limited sensitivity

ANN: Artificial neural network; AUROC: Area under the receiver operating characteristic curve; GB: Gradient boosting; LR: Logistic regression; ML: Machine learning; MLP: Multilayer perceptron; RF: Random forest; SVM: Support vector machine; Val: Validation cohort; XGB: Extreme gradient boosting.