Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma

Table 2 Performance metrics of six models in the training dataset

Model	AUC (95%CI)	Accuracy (95%CI)	Sensitivity (95%CI)	Specificity (95%CI)	F1 score (95%CI)
XGBoost	0.993 (0.984-1.000)	0.974 (0.969-0.979)	0.980 (0.971-0.988)	0.976 (0.966-0.986)	0.981 (0.977-0.985)
GNB	0.692 (0.624-0.761)	0.671 (0.660-0.683)	0.754 (0.689-0.820)	0.566 (0.486-0.646)	0.728 (0.705-0.750)
MLP	0.574 (0.501-0.648)	0.563 (0.516-0.611)	0.517 (0.274-0.761)	0.637 (0.410-0.863)	0.549 (0.411-0.686)
SVM	0.308 (0.240-0.376)	0.453 (0.391-0.514)	0.204 (-0.182-0.591)	0.800 (0.413-1.187)	NaN
Logistic	0.702 (0.634-0.770)	0.677 (0.657-0.696)	0.747 (0.654-0.841)	0.589 (0.499-0.679)	0.727 (0.689-0.765)
AdaBoost	0.814 (0.761-0.868)	0.725 (0.701-0.748)	0.661 (0.585-0.737)	0.825 (0.773-0.876)	0.737 (0.700-0.773)

95%CI: 95% confidence interval; AUC: Area under the curve; GNB: Complement NB; MLP: Multilayer perceptron; SVM: Support vector machine; NaN: Not a number.