A Novel RNA-Seq-Based Model for Preoperative Prediction of Lymph Node Metastasis in Oral Squamous Cell Carcinoma

Table 2

Comparison of the predictive performances of the machine learning model in the test set.

Model performances (HR, 95% CI)	RF	SVM	LR	XGBoost
Sensitivity	0.82 (0.75, 0.89)	0.80 (0.75, 0.85)	0.81 (0.74, 0.89)	0.72 (0.63, 0.81)
Specificity	0.67 (0.57, 0.78)	0.76 (0.68, 0.84)	0.76 (0.68, 0.84)	0.67 (0.57, 0.77)
PPV	0.77 (0.72, 0.83)	0.82 (0.77, 0.87)	0.82 (0.77, 0.88)	0.74 (0.67, 0.81)
NPV	0.76 (0.67, 0.84)	0.75 (0.71, 0.79)	0.78 (0.70, 0.83)	0.65 (0.56, 0.75)
Accuracy	0.75 (0.71, 0.80)	0.78 (0.75, 0.81)	0.79 (0.74, 0.83)	0.70 (0.62, 0.77)
AUC	0.82 (0.77, 0.88)	0.84 (0.80, 0.87)	0.84 (0.80, 0.89)	0.77 (0.71, 0.83)

RF: random forest; SVM: support vector machine; LR: logistic regression; HR: hazard ratio; CI: confidence interval; PPV: positive predictive value; NPV: negative predictive value; AUC: area under the curve.