Table 2.

Models’ performance comparison of machine learning models based on high-frequency glucose monitoring.

Models	Accuracy, median (IQR)	Precision, median (IQR)	Recall, median (IQR)	F1-score, median (IQR)	AUROCa, median (IQR)
SVMb	0.64 (0.61-0.66)	0.64 (0.62-0.67)	0.6 (0.59-0.61)	0.62 (0.6-0.65)	0.7 (0.69-0.72)
Random forest	0.68 (0.65-0.7)	0.65 (0.62-0.67)	0.7 (0.68-0.73)	0.67 (0.65-0.69)	0.75 (0.74-0.77)
ExtraTrees	0.69 (0.67-0.71)	0.69 (0.67-0.71)	0.66 (0.65-0.68)	0.67 (0.65-0.7)	0.75 (0.72-0.78)
XGBoostc	0.7 (0.68-0.72)	0.68 (0.68-0.69)	0.71 (0.7-0.72)	0.69 (0.68-0.69)	0.74 (0.73-0.75)
AdaBoost	0.7 (0.68-0.71)	0.69 (0.68-0.71)	0.67 (0.66-0.69)	0.69 (0.67-0.7)	0.76 (0.74-0.77)
Logistic	0.63 (0.62-0.63)	0.62 (0.61-0.62)	0.62 (0.61-0.63)	0.62 (0.62-0.63)	0.69 (0.67-0.7)

^aAUROC: area under the receiver operating characteristic curve.

^bSVM: support vector machine.

^cXGBoost: Extreme Gradient Boosting.