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

Table 7 The comparative results of models using all techniques and standard data mining models

Model	Accuracy	Sensitivity	Specificity	g-mean	AUC
DT_9	0.912	0.140	0.991	0.374	0.772
LR_9	0.913	0.156	0.990	0.394	0.829
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
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
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
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
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

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