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Table 1 Classification results according to SVM, PLS-DA and CART for discrimination of fascicular tissue of the nerve from the surrounding tissues

From: Nerve detection with optical spectroscopy for regional anesthesia procedures

Classification method Feature selection MCC ACC SENS (%) SPEC (%) PPV NPV TP FN FP TN
SVM Fit 0.711 0.854 82.6 88.8 0.901 0.806 580 122 64 508
SVM PCA 0.793 0.897 89.9 89.5 0.913 0.878 631 71 60 512
SVM Segments 0.779 0.890 88.6 89.5 0.912 0.865 622 80 60 512
SVM Combined 0.826 0.914 91.3 91.4 0.929 0.896 641 61 49 523
PLSDA 10PC’s 0.814 0.907 92.5 89.5 0.864 0.943 494 40 78 662
CART Fit 0.615 0.808 81.2 80.4 0.836 0.777 570 132 112 460
  1. For SVM different feature selection methods are used: fit parameters, PCA, segments and a combination of the last three. For PLSDA, 10 principal components have been used (10PC’s). For the CART analysis, the fit parameters have been used as features
  2. Matthews correlation coefficient (MCC see Eq. 2 text), accuracy (ACC = [TP + TN]/[TP + FN + FP + TN]), sensitivity (SENS = TP/[TP + FN]), specificity (SPEC = TN/[FP + TN]), positive predictive value (PPV = TP/[TP + FP]), negative predictive value (NPV = TN/[TN + FN]), true positive (TP), false negative (FN), false positive (FP), true negative (TN)