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Table 2 The performance of machine learning classifiers in the prediction of low-grade vs. high-grade fibrosis

From: Three-dimensional CT texture analysis of anatomic liver segments can differentiate between low-grade and high-grade fibrosis

Modela

RFC (Model 1)b

SVM (Model 2)b

RFC (Model 3)c

SVM (Model 4)c

Number of featuresd

2

18

28

66

Train AUC

0.95 (0.91–0.98)

0.88 (0.81–0.94)

0.84 (0.82–0.85)

0.91 (0.88–0.94)

Test AUC

0.90 (0.85–0.95)

0.76 (0.67–0.84)

0.88 (0.84–0.91)

0.90 (0.87–0.93)

Sensitivitye

86%

93%

86%

83%

Specificitye

78%

31%

92%

95%

NPVe

89%

73%

81%

78%

PPV e

73%

69%

94%

96%

  1. a Optimized for the classification of low-grade vs. high-grade fibrosis in liver segments;
  2. b The liver segments were randomly divided into equal size training and test set;
  3. c Segments of patients who had been scanned with a 64-slice scanner constituted the training set, and patients who had been scanned with a 16-slice scanner were assigned to the test set;
  4. dAfter cross-validated recursive feature elimination;
  5. e Calculated in the test set; RFC random forest classifier, SVM support vector machine classifier, NPV negative predictive value, PPV positive predictive value