Table 5 Performance summary of ML models in internal validation.

Models	AUC	95% CI		Sensitivity	Specificity	Accuracy	Log-loss	FP rate	Precision	AP	F1
Models	AUC	Lower	Upper	Sensitivity	Specificity	Accuracy	Log-loss	FP rate	Precision	AP	F1
LR	0.69	0.57	0.82	0.64	0.69	0.67	0.95	0.30	0.66	0.69	0.65
ElasticNet	0.93	0.87	0.99	0.91	0.91	0.91	0.81	0.08	0.91	0.93	0.91
Lasso	0.89	0.81	0.97	0.85	0.80	0.82	0.47	0.19	0.80	0.89	0.82
Ridge	0.89	0.82	0.97	0.85	0.80	0.82	0.47	0.18	0.80	0.87	0.82
RF	0.98	0.95	0.99	0.97	0.97	0.97	0.24	0.02	0.97	0.98	0.97
SVM	0.85	0.76	0.94	0.88	0.83	0.85	5.14	0.16	0.83	0.79	0.85
k-NN	0.80	0.69	0.90	0.88	0.47	0.67	0.61	0.52	0.61	0.76	0.72
NN	0.95	0.90	0.99	0.94	0.91	0.92	0.24	0.08	0.91	0.96	0.92
XGBoost	0.93	0.97	0.99	0.85	0.94	0.90	0.34	0.05	0.93	0.92	0.89

Quick links

Search