The use of class imbalanced learning methods on ULSAM data to predict the case–control status in genome-wide association studies

Journal of Big Data

Table 2 The performances of support vector machine with imbalanced learning methods

Imbalanced learning methods number of SNP 399935		Prediction class		PPV*	NPV**	Sensitivity	Specificity	F1 Score	Accuracy
SVM		Controls	Cases
Reel class	Controls	278	0	0.00	0.82	0.00	1.00	0.00	0.82
Reel class	Cases	57	0	0.00	0.82	0.00	1.00	0.00	0.82
SMOTE		Controls	Cases
Reel Class	Controls	287	0	1.00	0.85	0.83	1.00	0.90	0.91
Reel Class	Cases	47	237	1.00	0.85	0.83	1.00	0.90	0.91
SVM SMOTE		Controls	Cases
Reel Class	Controls	293	0	1.00	0.83	0.67	1.00	0.80	0.87
Reel Class	Cases	56	117	1.00	0.83	0.67	1.00	0.80	0.87
ADASYN		Controls	Cases
Reel Class	Controls	285	0	1.00	0.84	0.81	1.00	0.89	0.90
Reel Class	Cases	51	226	1.00	0.84	0.81	1.00	0.89	0.90
RUS		Controls	Cases
Reel Class	Controls	47	0	**	0.47	0.00	1.00	0.00	0.47
Reel Class	Cases	53	0	**	0.47	0.00	1.00	0.00	0.47