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Table 2 Best classification performance on BCDR database

From: A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis

  

Normal/Abnormal

Benign/Malignant

Embedded Method

AUC

98.16 (97.87−98.48)∗∗

92.08 (91.61−92.58)

 

Accuracy

97.31 (96.92−97.31)∗∗

88.46 (87.69−89.23)

 

Sensitivity

94.62 (93.85−94.62)

89.09 (87.27−90.91)

 

Specificity

100 (100−100)∗∗

88.00 (86.67−89.33)

Filter Method

AUC

98.67 (98.57−98.76)

92.13 (91.66−92.78)

 

Accuracy

96.92 (96.54−96.92)

87.69 (86.92−89.23)

 

Sensitivity

93.85 (93.85−94.62)

89.09 (87.27−90.91)

 

Specificity

99.23 (99.23−100)

87.33 (85.33−89.33)

  1. The classification performance calculated in correspondence with the best result highlighted in the 100 rounds of 10-fold cross-validation for increasing the number of selected features, are summarized. We tested the significance of the diversity of performance measures obtained with the two different feature selection techniques on the same classification problem. Statistical significance is measured with the Wilcoxon-Mann-Whitney test: ** p-value <0.01 (Bonferroni correction)
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BMC Bioinformatics

ISSN: 1471-2105

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