Table 2.

Model performances for predicting post-filter ionized calcium levels

Labels	Models	Precision (%)	Recall (%)	F1-score (%)	Accuracy (%)
“0”: < 0.25 mmol/L	AdaBoost	70.43	69.61	69.99	77.94
	XGBoost	83.73	79.41	81.20	86.76
	SVM	91.13	67.65	71.22	83.82
	Shallow neural network	90.76	90.77	90.77	90.76
“1”: 0.25–0.35 mmol/L	AdaBoost	67.39	59.21	60.04	76.32
	XGBoost	82.65	78.54	86.41	85.17
	SVM	92.93	77.41	79.72	86.25
	Shallow neural network	88.45	88.4	88.40	88.45
“2”: 0.35–0.5 mmol/L	AdaBoost	70.22	59.11	59.85	77.17
	XGBoost	83.77	80.92	82.17	83.77
	SVM	81.07	80.89	81.87	81.89
	Shallow neural network	83.74	80.92	82.17	88.77
“3”: > 0.5 mmol/L	AdaBoost	73.15	68.40	70.03	79.69
	XGBoost	83.91	81.94	82.85	87.50
	SVM	91.38	68.75	72.56	84.38
	Shallow neural network	88.98	88.96	88.96	88.96

The bold means the best performed model for each evaluation indicator