Table 2. Classification accuracies for the different machine learning models using optimal marker combinations

Method	Model	Accuracy	Specificity	Sensitivity	Precision	F1-score
RF	DT	1.00	1.00	1.00	1.00	1.00
	AB	1.00	1.00	1.00	1.00	1.00
	SVM	1.00	1.00	1.00	1.00	1.00
	QDA	1.00	1.00	1.00	1.00	1.00
	RF	1.00	1.00	1.00	1.00	1.00
	LDA	1.00	1.00	1.00	1.00	1.00
	KNN	1.00	1.00	1.00	1.00	1.00
	NB	1.00	1.00	1.00	1.00	1.00
AB	DT	1.00	1.00	1.00	1.00	1.00
	AB	1.00	1.00	1.00	1.00	1.00
	SVM	1.00	1.00	1.00	1.00	1.00
	QDA	1.00	1.00	1.00	1.00	1.00
	RF	1.00	1.00	1.00	1.00	1.00
	LDA	1.00	1.00	1.00	1.00	1.00
	KNN	1.00	1.00	1.00	1.00	1.00
	NB	1.00	1.00	1.00	1.00	1.00

DT, decision tree; AB, AdaBoost; SVM, support vector machine; QDA, quadratic discriminant analysis; RF, Random Forest; LDA, linear discriminant analysis; KNN, K-Nearest Neighbor; NB, Naïve Bayes.