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