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

Table 7 Kruskal-Wallis P-values when comparing logistic regression to each Machine learning model independently

Comparison	AUC-ROC P value	Accuracy P value	Sensitivity P value	Specificity P value
LR vs SVM	0.2454	0.2396	0.0139	0.0778
LR vs DET	0.4678	0.2186	0.8845	0.3836
LR vs RF	0.3865	0.5516	0.6631	0.7715
LR vs GB	0.1913	0.2425	0.7728	0.1465
LR vs XGB	0.5637	0.2367	0.7728	0.6612
LR vs MLP	0.8845	0.7702	0.1804	0.0384

LR: Logistic regression; SVM: Support vector machine; DET: Decision trees; RF: Random forest; GB: Gradient boost; XGB: Extreme gradient boost; MLP: Multilayer perceptron.