[Retracted] The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets

Table 9

The average results using classifiers (KNN, SVM, and DT) with models (baseline, Tml, RUS, ENN, and HDUS) on four datasets.

		Baseline	Tml	RUS	ENN	HDUS
KNN	Sensitivity (%)	30.70	36.55	58.37	47.02	62.28
	Specificity (%)	88.54	81.18	65.12	74.34	65.39
	Precision (%)	37.54	33.47	38.60	37.24	36.88
	F1_m (%)	33.59	34.92	46.06	40.82	45.76
	Bacc (%)	59.37	58.87	61.77	60.68	63.84
SVM	Sensitivity (%)	7.03	33.61	67.63	42.57	73.38
	Specificity (%)	96.66	88.71	60.01	80.62	60.36
	Precision (%)	20.06	37.86	38.49	38.73	39.10
	F1_m (%)	10.34	35.19	48.03	39.98	49.85
	Bacc (%)	51.85	61.16	63.94	61.69	66.87
DT	Sensitivity (%)	39.82	41.91	55.25	55.04	82.91
	Specificity (%)	75.34	70.51	54.02	65.89	57.44
	Precision (%)	36.60	33.61	30.10	36.66	41.65
	F1_m (%)	37.86	36.23	38.60	43.59	54.79
	Bacc (%)	57.58	56.21	54.64	60.47	70.17