Table 1 Evaluation of AMR classifiers for first-line and second-line anti-TB drugs (our ML methods VS the rule-based one Mykrobe predictor).

From: Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN

Methods

Precision (%)

Sensitivity (%)

Specificity (%)

Accuracy (%)

F1 (%)

G-mean (%)

First-line

INH

RF

95.2

98.7

91.7

96.1

97.0

95.1

LR

94.3

99.2

90.0

95.7

96.7

94.5

CNN

95.5

99.0

90.5

96.2

97.2

94.7

Mykrobe

92.9

99.2

95.3

96.2

95.9

97.2

PZA

RF

92.3

96.4

53.6

90.1

94.3

71.9

LR

93.0

95.5

58.0

90.0

94.2

74.4

CNN

93.2

96.5

56.1

90.5

94.8

73.6

Mykrobe

91.1

95.1

60.9

87.3

93.1

76.1

RIF

RF

94.3

97.0

88.6

94.1

95.6

92.7

LR

93.5

98.3

86.6

94.3

95.8

92.3

CNN

94.4

98.1

87.5

94.6

96.2

92.7

Mykrobe

92.4

95.0

92.3

92.5

93.7

93.6

EMB

RF

92.9

94.4

70.8

89.7

93.6

81.8

LR

93.1

93.4

72.1

89.2

93.3

82.1

CNN

93.1

94.5

71.7

90.0

93.8

82.3

Mykrobe

92.2

72.4

85.3

76.3

81.1

78.6

Second-line

AMK

RF

99.1

99.7

80.9

98.9

99.4

89.8

LR

99.2

99.9

82.8

99.2

99.5

91.0

CNN

99.2

100

82.9

99.2

99.6

91.1

Mykrobe

99.1

100

81.9

99.2

99.5

90.5

CM

RF

96.0

98.7

48.0

94.9

97.3

68.8

LR

96.1

99.9

49.0

96.1

98.0

70.0

CNN

96.1

99.9

49.0

96.1

98.0

70.0

Mykrobe

96.0

99.9

47.8

96.1

97.8

69.1

KM

RF

94.0

93.9

59.0

89.4

93.9

74.4

LR

94.5

95.1

63.0

91.0

94.8

77.4

CNN

93.0

98.4

49.8

92.1

95.6

70.0

Mykrobe

90.9

99.5

31.9

91.0

95.0

56.4

OFX

RF

97.5

98.5

64.5

96.4

98.0

79.7

LR

97.6

99.1

65.2

96.9

98.3

80.4

CNN

97.5

98.9

63.4

96.6

98.2

79.2

Mykrobe

98.5

95.9

78.1

94.8

97.1

86.6