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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