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

Table 3 Performance metrics of six models in the validation dataset

Model	AUC (95%CI)	Accuracy (95%CI)	Sensitivity (95%CI)	Specificity (95%CI)	F1 score (95%CI)
XGBoost	0.734 (0.601-0.867)	0.683 (0.658-0.708)	0.662 (0.596-0.729)	0.782 (0.717-0.847)	0.694 (0.636-0.752)
GNB	0.657 (0.512-0.802)	0.618 (0.583-0.653)	0.647 (0.443-0.851)	0.662 (0.464-0.861)	0.641 (0.535-0.748)
MLP	0.548 (0.396-0.699)	0.514 (0.452-0.575)	0.575 (0.398-0.752)	0.627 (0.491-0.764)	0.577 (0.471-0.682)
SVM	0.363 (0.217-0.509)	0.446 (0.381-0.511)	0.452 (0.035-0.870)	0.617 (0.188-1.046)	NaN
Logistic	0.661 (0.517-0.806)	0.632 (0.602-0.661)	0.797 (0.709-0.886)	0.525 (0.414-0.637)	0.728 (0.706-0.751)
AdaBoost	0.649 (0.504-0.793)	0.618 (0.569-0.667)	0.591 (0.330-0.852)	0.780 (0.610-0.950)	0.617 (0.403-0.831)

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