Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma

Table 4 Performance of the ML models

Model	Sensitivity	Specificity	Accuracy	AUROC	F1-score
Kernel SVM	0.424	0.824	0.726	0.708	0.431
Logistic regression	0.700	0.766	0.749	0.801	0.578
Decision tree	0.520	0.789	0.723	0.697	0.479
KNN	0.533	0.783	0.721	0.658	0.484
Random forest	0.650	0.830	0.786	0.824	0.598
Gradient boost	0.594	0.877	0.808	0.826	0.603
AdaBoost	0.560	0.878	0.800	0.818	0.579
XGBoost	0.529	0.863	0.781	0.809	0.543
LightGBM	0.536	0.863	0.783	0.815	0.547

ISSN: 1472-6947