Table 3.

The optimal classifier sampling combination results

Machine learning models	Resampling techniques	Step	F1 (Mean ± SD)	AUC (Mean ± SD)	G-means (Mean ± SD)
Random Forest Classifier	SMOTEENN	before	70.77 ± 3.57	77.44 ± 2.15	74.03 ± 2.89
		after	78.27 ± 1.54	87.18 ± 1.12	86.47 ± 1.28
MLP Classifier	SMOTEENN	before	42.22 ± 5.39	64.63 ± 2.24	54.88 ± 3.94
		after	71.33 ± 1.99	88.76 ± 1.17	88.52 ± 1.28
Gradient Boosting	SMOTE	before	65.77 ± 1.77	74.65 ± 1.01	70.23 ± 1.46
		after	71.63 ± 2.42	85.47 ± 1.66	84.71 ± 1.88
Random Forest Classifier	SMOTE	before	70.77 ± 3.57	77.44 ± 2.15	74.03 ± 2.89
		after	82.18 ± 2.76	85.97 ± 1.84	84.85 ± 2.14
Decision Tree Classifier	SMOTE	before	62.01 ± 2.95	80.69 ± 1.71	79.33 ± 2.01
		after	63.88 ± 2.84	83.59 ± 2.11	82.84 ± 2.38
MLP Classifier	SMOTE	before	40.60 ± 5.43	63.8 ± 2.3	53.28 ± 4.13
		after	79.85 ± 3.91	89.7 ± 2.54	89.31 ± 2.78
Random Forest Classifier	Random Over Sampling	before	70.77 ± 3.57	77.44 ± 2.15	74.03 ± 2.89
		after	78.56 ± 3.60	82.72 ± 2.51	80.85 ± 3.11
MLP Classifier	Random Over Sampling	before	42.25 ± 2.41	64.64 ± 1.17	54.99 ± 2.14
		after	82.97 ± 2.46	89.25 ± 1.57	88.73 ± 1.75
Gradient Boosting	ADASYN	before	65.77 ± 1.77	74.65 ± 1.01	70.23 ± 1.46
		after	68.23 ± 0.98	85.17 ± 1.29	84.52 ± 1.49
Random Forest Classifier	ADASYN	before	70.77 ± 3.6	77.44 ± 2.15	74.03 ± 2.89
		after	81.24 ± 3.47	86.02 ± 2.42	84.93 ± 2.84
MLP Classifier	ADASYN	before	40.24 ± 4.98	63.52 ± 2.03	52.7 ± 3.6
		after	82.17 ± 3.38	89.61 ± 2.09	89.15 ± 2.31
Gradient Boosting	KMeansSMOTE	before	65.77 ± 1.77	74.65 ± 1.01	70.23 ± 1.46
		after	69.08 ± 4.15	77.04 ± 2.66	73.53 ± 3.52
Random Forest Classifier	KMeansSMOTE	before	70.77 ± 3.57	77.44 ± 2.15	74.03 ± 2.89
		after	74.66 ± 4.36	79.92 ± 2.82	77.28 ± 3.59
MLP Classifier	KMeansSMOTE	before	38.66 ± 4.6	62.84 ± 1.97	51.32 ± 4.03
		after	78.33 ± 6.98	88.25 ± 2.25	87.73 ± 2.42