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Table 5 Predictive performance of three classifiers: SVM-LASSO, SVM-RFE, and SVM-ReliefF

From: Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Classifier SVM-LASSO SVM-RFE SVM-ReliefF
Training cohort Validation cohort Testing cohort Training cohort Validation cohort Testing cohort Training cohort Validation cohort Testing cohort
AUC 0.838 [0.721–0.934] 0.795 [0.674–0.899] 0.754 [0.652–0.889] 0.842 [0.747–0.945] 0.803 [0.687–0.904] 0.761 [0.648–0.893] 0.860 [0.759–0.963] 0.824 [0.736–0.915] 0.787 [0.710–0.892]
Accuracy (%) 76.70 [68.05–85.26] 73.94 [73.49–89.75] 68.78 [55.67–82.89] 77.36 [65.35–86.25] 74.11 [62.98–87.19] 71.58 [59.87–83.74] 83.61 [75.85–92.65] 77.14 [69.38–86.05] 73.42 [61.63–82.66]
Sensitivity (%) 77.57 [64.45–87.31] 73.48 [60.67–86.48] 70.07 [61.71–82.99] 80.33 [69.35–89.79] 75.47 [63.78–89.36] 71.25 [61.36–80.74] 84.78 [74.36–93.74] 78.72 [64.35–91.48] 82.15 [73.74–91.92]
Specificity (%) 81.22 [72.19–89.24] 79.63 [61.34–70.49] 80.30 [71.58–89.43] 84.31 [73.74–92.18] 83.04 [71.39–95.46] 79.22 [66.57–88.34] 82.67 [74.35–93.14] 80.33 [69.74–92.26] 76.48 [63.36–89.17]
PPV (%) 76.88 [62.38–85.69] 73.86 [59.67–88.74] 69.71 [60.98–78.35] 79.46 [64.38–91.59] 74.78 [62.88–86.74] 72.98 [67.64–83.35] 77.79 [69.16–85.97] 75.98 [68.30–83.57] 74.82 [65.87–83.64]
NPV (%) 84.12 [76.41–95.37] 80.48 [71.70–92.26] 79.25 [66.28–91.33] 85.34 [75.65–96.43] 83.92 [74.54–91.35] 80.25 [72.36–92.17] 83.90 [72.74–95.46] 80.43 [71.32–89.91] 82.62 [71.64–92.35]
  1. Data in parentheses are 95% confidence intervals
  2. PPV positive predictive value, NPV negative predictive value