Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

Table 4 The evaluation indicators of machine learning models in testing dataset

Models	AUC	log-loss	Accuracy	Sensitivity	Specificity	Precision	F1 score
LR	0.842 (95% CI: 0.783–0.901)	0.513	0.766	0.848	0.751	0.378	0.523
SVM	0.834 (95% CI: 0.774–0.894)	0.344	0.748	0.879	0.724	0.362	0.513
ANN	0.890 (95% CI: 0.836–0.944)	0.296	0.858	0.333	0.951	0.551	0.415
RF	0.926 (95% CI: 0.879–0.974)	0.358	0.862	0.909	0.854	0.527	0.667
XGBoost	0.930 (95% CI: 0.878–0.982)	0.277	0.876	0.818	0.886	0.563	0.667
LightGBM^a	0.940 (95% CI: 0.900–0.980)	0.218	0.913	0.758	0.941	0.695	0.725

font bold: the optimal values; ^athe optimal model. LR logistic regression, SVM support vector machine, ANN artificial neural network, RF random forest, XGBoost extreme gradient boosting, LightGBM light gradient boosting machine

ISSN: 1472-6947