Skip to main content

Table 4 Performance of the two models, evaluated on an external dataset

From: Monitoring mandibular movements to detect Cheyne-Stokes Breathing

Metrics Scale (Worst-Best) Logistic model Decision tree model
ROC-AUC 0.5–1 0.93 0.92
Balanced Accuracy 0–1 0.92 0.92
True positive rate (Sensitivity) 0–1 0.92 0.92
True negative rate (Specificity) 0–1 0.92 0.92
Positive predictive value 0–1 0.93 0.93
Negative predictive value 0–1 0.91 0.91
False negative rate 1-0 0.08 0.08
False positive rate 1-0 0.08 0.08
Balance error 1-0 0.08 0.08
Mean misclassification error index 1-0 0.08 0.08