Table 2

Comparison of eight models to predict LVO in the test set

	AUC (95% CI)	SEN	SPE	Accuracy
RF	0.831 (0.819 to 0.843)	0.721	0.827	0.772
GBM	0.831 (0.820 to 0.843)	0.721	0.825	0.772
XGBoost	0.831 (0.820 to 0.844)	0.715	0.825	0.770
LightGBM	0.828 (0.816 to 0.840)	0.721	0.826	0.774
Ada boosting	0.828 (0.817 to 0.841)	0.704	0.824	0.765
ANN	0.819 (0.817 to 0.842)	0.740	0.781	0.761
LR	0.790 (0.778 to 0.804)	0.735	0.746	0.740
KNN	0.774 (0.762 to 0.789)	0.685	0.769	0.727

ANN, artificial neural network; AUC, area under the curve; GBM, gradient boosting machine; KNN, K-Nearest Neighbour; LR, logistic regression; LVO, large vessel occlusion stroke; RF, random forests; SEN, sensitivity; SPE, specificity; XGBoost, extreme gradient boosting.