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% |
- a Optimized for the classification of low-grade vs. high-grade fibrosis in liver segments;
- b The liver segments were randomly divided into equal size training and test set;
- 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;
- dAfter cross-validated recursive feature elimination;
- e Calculated in the test set; RFC random forest classifier, SVM support vector machine classifier, NPV negative predictive value, PPV positive predictive value