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

Table 6 Sensitivity and specificity metrics for the different delayed graft function classification models and different combinations of predictor variables

Metric	Sensitivity				Specificity
Model and data	D	DT	DR	DTR	D	DT	DR	DTR
LR	0.33	0.44	0.18	0.56	0.67	0.71	0.95	0.67
SVM	0	0	0	0	1	1	0.93	0.93
DET	0.44	0.29	0.38	0.50	0.69	0.64	0.73	0.47
RF	0.44	0.50	0.32	0.52	0.89	0.84	0.78	0.49
GB	0.21	0.50	0	0.47	0.96	0.73	0.98	0.69
XGB	0.09	0.50	0.41	0.61	0.98	0.73	0.71	0.64
MLP	0.53	0.56	0.56	0.44	0.67	0.64	0.51	0.53

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.