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 5 The performances of logistic regression with imbalanced learning methods

Imbalanced learning methods number of SNP 399935		Prediction Class		PPV*	NPV**	Sensitivity	Specificity	F1 Score	Accuracy
LR		Controls	Cases
Reel class	Controls	276	2	0.00	0.82	0.00	0.99	0.00	0.82
Reel class	Cases	57	0	0.00	0.82	0.00	0.99	0.00	0.82
SMOTE		Controls	Cases
Reel class	Controls	284	0	0.90	1.00	1.00	0.91	0.95	0.95
Reel class	Cases	26	262	0.90	1.00	1.00	0.91	0.95	0.95
SVM SMOTE		Controls	Cases
Reel class	Controls	291	2	0.98	0.86	0.74	0.99	0.84	0.90
Reel class	Cases	44	129	0.98	0.86	0.74	0.99	0.84	0.90
ADASYN		Controls	Cases
Reel class	Controls	285	0	1.00	0.89	0:87	1.00	0.93	0.93
Reel class	Cases	35	242	1.00	0.89	0:87	1.00	0.93	0.93
RUS		Controls	Cases
Reel class	Controls	28	19	0.53	0.47	0.41	0.59	0.46	0.50
Reel class	Cases	31	22	0.53	0.47	0.41	0.59	0.46	0.50