Machine learning-based prediction model for late recurrence after surgery in patients with renal cell carcinoma

Table 4 Performance of the machine learning algorithms

Model	Sensitivity	Specificity	Accuracy	AUROC	F1-score
Kernel SVM	0.551	0.852	0.835	0.702	0.579
Logistic regression	0.653	0.802	0.793	0.727	0.599
KNN	0.408	0.881	0.855	0.644	0.587
Naïve Bayes	0.612	0.805	0.795	0.709	0.566
Random forest	0.490	0.834	0.815	0.662	0.566
Gradient boost	0.531	0.868	0.849	0.699	0.576
AdaBoost	0.673	0.807	0.799	0.740	0.609
XGBoost	0.633	0.807	0.797	0.720	0.587

ISSN: 1472-6947