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Table 4 Results of criteria evaluated in the proposed method (in percentage)

From: Improving the performance of support-vector machine by selecting the best features by Gray Wolf algorithm to increase the accuracy of diagnosis of breast cancer

 

TN

FN

FP

TP

ACC

AUC

Recall

Specificity

Precision

Precision

F-measure

Without selected features

 First scenario

99

3

5

173

97.143

99.845

98.295

95.192

98.44

93.868

98.367

 Second scenario

75

6

2

127

96.19

99.22

95.489

97.403

97.19

91.962

96.331

 Third scenario

40

3

3

94

95.714

99.315

96.907

93.023

96.90

89.930

96.903

With selected features

 First scenario

98

3

6

173

96.143

97.362

98.295

94.231

96.64

93.100

97.460

 Second scenario

73

5

4

128

95.714

98.071

96.241

94.805

96.96

90.802

96.599

 Third scenario

43

0

0

97

100

100

100

100

100

100

100