Table 3.

Performance of the logistic regression model in the training, validation, and test sets

Indicators	Training Set	Validation Set	Test Set
AUC(95%CI)	0.80(0.79–0.82)	0.80(0.77–0.84)	0.88 (0.81–0.94)
Cut-off(95%CI)	0.19(0.16–0.21)	0.19(0.16–0.21)	0.25
Accuracy(95%CI)	0.70(0.67–0.73)	0.67(0.64–0.71)	0.79
Sensitivity(95%CI)	0.82(0.76–0.87)	0.76(0.65–0.87)	0.78
Specificity (95%CI)	0.67(0.61–0.72)	0.65(0.60–0.71)	0.79
PPV(95%CI)	0.42(0.38–0.45)	0.38(0.35–0.41)	0.52
NPV(95%CI)	0.93(0.92–0.94)	0.91(0.88–0.94)	0.93
F1 score(95%CI)	0.55(0.53–0.56)	0.50(0.46–0.55)	0.62
Kappa(95%CI)	0.36(0.33–0.38)	0.30(0.24–0.36)	0.48

Values are presented as point estimates with 95% confidence intervals (95% CI)

AUC Area Under the Curve, PPV Positive Predictive Value, NPV Negative Predictive Value; CI Confidence Interval