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

Table 5 The area under the receiving operating curve and Accuracy metrics for the different delayed graft function classification models and different combinations of predictor variables

Metric	AUC-ROC				Accuracy
Model and data	D	DT	DR	DTR	D	DT	DR	DTR
LR	0.49	0.68	0.53	0.67	0.51	0.58	0.58	0.62
SVM	0.35	0.62	0.51	0.51	0.57	0.57	0.53	0.53
DET	0.67	0.45	0.58	0.51	0.58	0.49	0.58	0.48
RF	0.78	0.71	0.57	0.52	0.70	0.70	0.58	0.50
GB	0.81	0.70	0.67	0.62	0.63	0.63	0.56	0.60
XGB	0.75	0.66	0.60	0.62	0.60	0.63	0.58	0.61
MLP	0.68	0.70	0.50	0.47	0.61	0.61	0.53	0.49

AUC-ROC: Area under the receiving operating curve; D: Donor data; DT: Donor and transplant data; DR: Donor and recipient data; DTR: Donor, transplant and recipient date; LR: Logistic regression; SVM: Support vector machine; DET: Decision trees; RF: Random forest; GB: Gradient boost; XGB: Extreme gradient boost; MLP: Multilayer perceptron.