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Table 3 Performance measures of data mining algorithm at different levels of significance over Alzheimer’s dataset

From: Comparative study of classification algorithms for immunosignaturing data

SIGNIFICANCE

p < 5 x 10-5

p < 5 x 10-4

p < 5 x 10-3

p < 5 x 10-2

 

Algorithm

Acc.

Sp

Sn

AUC

Acc.

Sp

Sn

AUC

Acc.

Sp

Sn

AUC

Acc

Sp

Sn

AUC

Avg.

Naïve Bayes

100

100

100

1.00

91.3

82.0

100

0.96

91.3

82.0

100

0.96

86.5

91.0

84.0

0.94

93.4

Logistic. R

95.0

90.0

100

0.99

95.7

90.0

100

0.97

91.3

90.0

91.7

0.90

91.3

90.0

91.7

0.90

93.3

MLP

91.3

90.9

91.7

0.97

95.6

90.9

100

0.97

87.0

90.9

83.3

0.97

dnf

dnf

dnf

dnf

92.7*

VFI

91.3

90.9

91.7

0.87

95.7

90.9

100

0.92

91.3

81.8

100

0.89

91.3

81.8

100

1.00

92.2

KNN

91.3

90.9

91.7

0.93

95.6

90.9

100

0.93

86.9

90.9

83.3

0.95

91.3

90.9

91.7

0.92

91.8

K-means

82.6

100

66.7

0.83

91.3

90.9

100

0.91

95.7

90.9

100

0.96

91.3

81.8

100

0.90

90.7

Hyper Pipes

91.3

81.8

100

0.98

95.7

90.9

100

0.97

91.3

81.8

100

0.95

73.9

81.8

66.7

0.90

89.7

SVM

87.0

90.9

83.3

0.87

95.7

90.9

100

0.95

82.6

81.8

83.3

0.83

87.0

81.8

91.7

0.87

88.0

Bayes Net

91.3

81.8

100

0.96

91.3

90.9

91.7

0.95

87.0

81.8

91.7

0.86

78.3

81.8

75.0

0.84

87.7

R. Forest

86.9

81.8

91.7

0.94

82.6

81.8

83.3

0.93

73.9

72.7

75.0

0.89

72.6

81.8

75.0

0.84

82.4

K star

95.7

90.9

100

0.98

91.3

90.9

91.7

0.94

78.2

81.8

75.0

0.86

56.5

18.2

91.7

0.64

81.5

SLR

86.9

81.8

91.7

0.96

73.9

72.7

75.0

0.82

60.9

63.6

58.3

0.80

52.2

54.5

50.0

0.69

71.8

Random Tree

78.3

72.7

83.3

0.78

60.9

54.5

66.7

0.61

73.9

63.6

83.3

0.74

73.9

81.8

66.7

0.74

71.7

ASC

73.9

63.6

83.3

0.61

68.9

63.6

58.3

0.56

73.9

81.8

66.7

0.75

78.2

63.9

91.7

0.61

70.0

J48

73.9

63.6

83.3

0.61

60.9

63.6

58.3

0.56

73.9

81.8

70.0

0.75

78.3

63.6

91.7

0.61

69.7

M5P

69.5

54.5

83.3

0.80

52.2

45.5

58.3

0.73

56.5

45.5

66.7

0.43

56.5

36.4

75.0

0.44

58.7

LDA

69.6

72.7

66.7

0.81

34.8

40.0

75.0

0.45

34.8

0.0

100

0.30

30.4

100

0.0

0.52

52.0

  1. Acc: Accuracy, Sp: Specificity, Sn: Sensitivity, AUC: Area under ROC curve, Avg: Average score in % for each algorithms, dnf: Did not Finish”, * denotes Avg. from 3 significance levels. Measures >90% are marked in bold.