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Table 1 Classification metrics of SVM

From: RNA profiling of blood platelets noninvasively differentiates colorectal cancer from healthy donors and noncancerous intestinal diseases: a retrospective cohort study

Performance metrics The classification metrics of SVM across three data sets
Training set (LOOCV, n = 202) Internal validation set (n = 120) External validation set (n = 101)
Accuracy (95% CI) 0.876 (0.823–0.918) 0.875 (0.802–0.928) 0.861 (0.778–0.922)
Sensitivity (95% CI) 0.975 (0.913–0.997) 0.885 (0.766–0.956) 0.761 (0.612–0.874)
Specificity (95% CI) 0.811 (0.731–0.877) 0.868 (0.764–0.938) 0.945 (0.849–0.989)
Positive predicted value 0.772 (0.678–0.850) 0.836 (0.712–0.922) 0.921 (0.786–0.983)
Negative predicted value 0.980 (0.930–0.998) 0.908 (0.810–0.965) 0.825 (0.709–0.909)
Kappaa 0.752 0.747 0.717
F1a 0.862 0.860 0.833
  1. aKappa measured the agreement between the predicted classification with true labels. F1 was the harmonic average of precision (positive predicted value) and recall rates (sensitivity)