Table 3.

Values of AUC and accuracy of machine learning analysis when comparing each group or their combinations.

Classification	ML Model Which Showed the Best Result	AUC	Accuracy (%)
A vs. B vs. C vs. D	XGB + RF	0.8546	60.85
A vs. B vs. C + D	RF	0.8105	62.34
A vs. B + C vs. D	RF	0.8075	61.32
A + B vs. C vs. D	XGB + RF	0.8925	73.40
A + B vs. C + D	RF	0.8103	72.68
A + B + C vs. D	RF	0.8250	74.47

Note: A = normal group, B = possible group, C = probable group, D = confirmed group. Abbreviations: AUC = area under the curve; XGB = XGBoost; RF = random forest; SVM = support vector machine.