Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients

Table 2

Prediction results using various scoring methods. The number of hidden nodes for ELM algorithms was 20. The size of ensemble for both V-ELM and SV-ELM algorithms was 15. The initial ensemble size for SV-ELM was 25.

Method	AUC (95% CI)	Cutoff	Sensitivity (95% CI)	Specificity (95% CI)	PPV (95% CI)	NPV (95% CI)
SVM	0.733 (0.654–0.812)	64	76.9% (65.5%–88.4%)	63.0% (60.0%–66.0%)	10.0% (7.1%–12.9%)	98.1% (97.0%–99.2%)
ELM	0.736 (0.656–0.815)	62	75.0% (63.2%–86.8%)	61.9% (58.8%–64.9%)	9.5% (6.7%–12.4%)	97.9% (96.7%–99.0%)
V-ELM	0.749 (0.671–0.827)	64	78.8% (67.7%–89.9%)	63.8% (60.8%–66.8%)	10.4% (7.4%–13.5%)	98.3% (97.3%–99.3%)
SV-ELM	0.754 (0.676–0.832)	64	78.8% (67.7%–89.9%)	64.7% (61.7%–67.8%)	10.7% (7.6%–13.8%)	98.3% (97.3%–99.3%)