Machine Learning-Based Forecast of Hemorrhagic Stroke Healthcare Service Demand considering Air Pollution

Table 5

Statistics on the performance of warm season models considering air pollution among the machine learning methods.

Model	M-AUC	M-Sens	M-Spec	SD-AUC	SD-Sens	SD-Spec
LR	0.7369	0.4684	0.8708	0.0276	0.0687	0.0137
RF	0.6811	0.2520	0.8368	0.0434	0.1096	0.0068
SVMLinear	0.6743	0.1795	0.7483	0.0409	0.0467	0.1159
KNN	0.6681	0.2558	0.8551	0.0371	0.1312	0.0224
XGBLinear	0.6601	0.2915	0.8448	0.0329	0.0574	0.0068
XGBTree	0.6599	0.2195	0.8563	0.0373	0.0838	0.0126

M-AUC, M-Sens, and M-Spec denote the average area under the curve (AUC), sensitivity, and specificity, respectively; SD-AUC, SD-Sens, and SD-Spec denote the standard deviation of the AUC, sensitivity, and specificity, respectively. LR, logistic regression; RF, random forest; SVMLinear, support-vector machines with linear kernel; KNN, k-nearest neighbor algorithm; XGBTree, extreme gradient boosting decision tree; XGBLinear, extreme gradient boosting linear model.