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Table 2 Evaluation of the classifiers for each group and comparison among KNN, Random Forest, Naïve Bayes and Support Vector Machine

From: Identification of arthropathy and myopathy of the temporomandibular syndrome by biomechanical facial features

GroupsSensitivity (± STD)Specificity (± STD)Precision (± STD)Accuracy (± STD)
KNN
 AG0.9737 (0.0582)0.9756 (0.0221)0.9320 (0.0566)0.9701 (0.0219)
 MG0.8703 (0.0793)0.9897 (0.0142)0.9676 (0.0425)0.9599 (0.0221)
 CG0.9769 (0.0265)0.9411 (0.0398)0.9445 (0.0353)0.9590 (0.0234)
Random forest
 AG0.6852 (0.0030)0.8839 (0.0011)0.6642 (0.0021)0.8342 (0.0009)
 MG0.6650 (0.0030)0.8961 (0.0009)0.6817 (0.0021)0.8383 (0.0009)
 CG0.7900 (0.0018)0.7951 (0.0018)0.7947 (0.0014)0.7926 (0.0011)
Naïve Bayes
 AG0.7132 (0.0052)0.8634 (0.0022)0.6391 (0.0040)0.8258 (0.0020)
 MG0.5601 (0.0056)0.9424 (0.0018)0.7716 (0.0055)0.8468 (0.0018)
 CG0.8411 (0.0029)0.7691 (0.0033)0.7864 (0.0024)0.8051 (0.0020)
Support vector machine
 AG0.7942 (0.0049)0.7894 (0.0045)0.7925 (0.0040)0.7918 (0.0037)
 MG0.7894 (0.0045)0.7942 (0.0049)0.7963 (0.0042)0.7918 (0.0037)
 CG0.8846 (0.0030)0.7718 (0.0050)0.7989 (0.0037)0.8282 (0.0029)
  1. STD standard deviation