Table 2.

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

Groups	Sensitivity (± STD)	Specificity (± STD)	Precision (± STD)	Accuracy (± STD)
KNN
AG	0.9737 (0.0582)	0.9756 (0.0221)	0.9320 (0.0566)	0.9701 (0.0219)
MG	0.8703 (0.0793)	0.9897 (0.0142)	0.9676 (0.0425)	0.9599 (0.0221)
CG	0.9769 (0.0265)	0.9411 (0.0398)	0.9445 (0.0353)	0.9590 (0.0234)
Random forest
AG	0.6852 (0.0030)	0.8839 (0.0011)	0.6642 (0.0021)	0.8342 (0.0009)
MG	0.6650 (0.0030)	0.8961 (0.0009)	0.6817 (0.0021)	0.8383 (0.0009)
CG	0.7900 (0.0018)	0.7951 (0.0018)	0.7947 (0.0014)	0.7926 (0.0011)
Naïve Bayes
AG	0.7132 (0.0052)	0.8634 (0.0022)	0.6391 (0.0040)	0.8258 (0.0020)
MG	0.5601 (0.0056)	0.9424 (0.0018)	0.7716 (0.0055)	0.8468 (0.0018)
CG	0.8411 (0.0029)	0.7691 (0.0033)	0.7864 (0.0024)	0.8051 (0.0020)
Support vector machine
AG	0.7942 (0.0049)	0.7894 (0.0045)	0.7925 (0.0040)	0.7918 (0.0037)
MG	0.7894 (0.0045)	0.7942 (0.0049)	0.7963 (0.0042)	0.7918 (0.0037)
CG	0.8846 (0.0030)	0.7718 (0.0050)	0.7989 (0.0037)	0.8282 (0.0029)

STD standard deviation