An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data

Table 13 The comparative results of models using feature pruning

Model	Accuracy	Sensitivity	Specificity	g-mean	AUC
S_DT_9	0.791	0.475	0.823	0.626	0.700
S_LR_9	0.759	0.645	0.771	0.705	0.783
S_pDT	0.728	0.703	0.731	0.717	0.770
S_rLR	0.747	0.717	0.750	0.734	0.811
C_DT_9	0.772	0.669	0.792	0.727	0.758
C_LR_9	0.752	0.752	0.752	0.752	0.829
C_pDT	0.740	0.748	0.740	0.744	0.795
C_rLR	0.770	0.719	0.776	0.747	0.824
U_DT_9	0.748	0.748	0.749	0.748	0.798
U_LR_9	0.749	0.732	0.767	0.749	0.825
U_pDT	0.740	0.749	0.731	0.740	0.791
U_rLR	0.745	0.703	0.787	0.743	0.823
Ba_DT_9	0.911	0.151	0.990	0.386	0.797
Ba_LR_9	0.913	0.157	0.990	0.394	0.829
Ba_pDT	0.911	0.107	0.994	0.324	0.724
Ba_rLR	0.912	0.142	0.991	0.377	0.823
Ad_DT_9	0.902	0.197	0.974	0.438	0.752
Ad_LR_9	0.913	0.157	0.990	0.394	0.787
Ad_pDT	0.911	0.161	0.988	0.397	0.822
Ad_rLR	0.910	0.130	0.990	0.359	0.745

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