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Table 3 Contingency table for the second feature set

From: Multivariate classification of urine metabolome profiles for breast cancer diagnosis

 

Actual

Output

Accuracy

Sensitivity

Specificity

  

Cancer

Normal

   

Decision Tree

      

Confidence=0.25

Cancer

46

4

90.06%

92.00%

88.00%

Pruning=true

Normal

6

44

   

Random Forest

      

Tree=500

Cancer

46

4

91.02%

92.00%

90.00%

Feature=6

Normal

5

45

   

Support Vector Machine (Linear)

      

Cost=25

Cancer

48

2

91.41%

96.00%

86.00%

Gamma=0.33

Normal

7

43

# of Support Vectors: 26

Support Vector Machine (Gaussian)

      

Cost=45

Cancer

46

4

91.02%

92.00%

90.00%

Gamma=0.33

Normal

5

45

# of Support Vectors: 26

Classification results for the second feature set (Table 1B)

  1. Contingency table showing number of cases classified for each of the diagnostic classes for the second feature set (Table 1B).