Table 5.

Performance results based on test data using four classifiers: KNN, logistic regression, SVM, random forest

Classifier	Data augmentation	Precision	Recall	F1	AUC
KNN	Without augmentation	0.73	0.58	0.64	0.753
		0.87	0.93	0.9
	SMOTE	0.44	0.84	0.58	0.752
		0.93	0.67	0.78
	Positive augmentation	0.57	0.68	0.62	0.758
		0.89	0.68	0.87
	4× augmentation	0.57	0.84	0.62	0.758
		0.89	0.89	0.87
Logistic	Without augmentation	0.76	0.54	0.63	0.744
		0.87	0.95	0.91
	SMOTE	0.61	0.82	0.64	0.79
		0.92	0.76	0.84
	Positive augmentation	0.57	0.66	0.61	0.754
		0.89	0.84	0.86
	4× augmentation	0.63	0.72	0.67	0.792
		0.91	0.87	0.89
SVM	Without augmentation	0.63	0.73	0.68	0.798
		0.91	0.86	0.89
	SMOTE	0.63	0.77	0.69	0.813
		0.92	0.86	0.89
	Positive augmentation	0.59	0.64	0.61	0.75
		0.88	0.86	0.87
	4× augmentation	0.61	0.71	0.66	0.784
		0.9	0.86	0.88
Random forest	Without augmentation	0.69	0.38	0.49	0.663
		0.83	0.95	0.88
	SMOTE	0.62	0.5	0.56	0.704
		0.85	0.9	0.88
	Positive augmentation	0.54	0.63	0.58	0.73
		0.88	0.83	0.85
	4× augmentation	0.59	0.54	0.56	0.71
		0.86	0.88	0.87