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Table 2 The highest values for accuracy, AUC, F-measure, precision, recall, sensitivity, and specificity for predicting responders vs. non-responders (A) and responders vs. relapsers (B) groups

From: Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms

A

 

Subtype 1a (Responders vs. Non-Responders)

Subtype 1b (Responders vs. Non-Responders)

Bayes

Neural Networks

SVM

Decision Trees

Bayes

Neural Networks

SVM

Decision Trees

Database

Chi Squared

SVM

Relief

PCA

SVM

SVM

Relief

Gini Index

Algorithm

Naive Bayes (Kernel)

AutoMLp

SVM

DT Parallel Gini Index

Naive Bayes (Kernel)

AutoMLp

SVM

DT Random Forest Info Gain

Accuracy

74.17%

76.67%

74.17%

69.17%

89.17%

85.00%

75.00%

80.00%

AUC

0.84

0.68

0.75

0.59

0.94

0.94

0.84

0.83

AUC (optimistic)

0.84

0.68

0.75

0.83

0.94

0.94

0.84

0.85

AUC (pessimistic)

0.84

0.68

0.75

0.58

0.94

0.94

0.84

0.80

F-Measure

0.78

0.82

0.80

0.73

0.92

0.87

0.80

0.86

Precision

0.84

0.82

0.80

0.80

0.93

0.94

0.81

0.87

Recall

0.73

0.82

0.88

0.73

0.93

0.83

0.83

0.90

Sensitivity

0.73

0.82

0.88

0.73

0.93

0.83

0.83

0.90

Specificity

0.85

0.75

0.60

0.65

0.85

0.80

0.50

0.65

B

 

Subtype 1a (Responders vs. Relapsers)

Subtype 1b (Responders vs. Relapsers)

Bayes

Neural Networks

SVM

Decision Trees

Bayes

Neural Networks

SVM

Decision Trees

Database

Chi Squared

SVM

Relief

PCA

SVM

SVM

Relief

Gini Index

Algorithm

Naive Bayes (Kernel)

AutoMLp

SVM

DT Parallel Gini Index

Naive Bayes (Kernel)

AutoMLp

SVM

DT Random Forest Info Gain

Accuracy

82.50%

79.17%

82.50%

81.67%

78.33%

78.33%

84.17%

81.67%

AUC

0.89

0.79

0.82

0.61

 

0.00

 

0.66

AUC (optimistic)

0.89

0.79

0.82

0.91

 

0.85

 

0.85

AUC (pessimistic)

0.89

0.79

0.82

0.74

 

0.15

 

0.47

F-Measure

0.84

0.86

0.86

0.84

0.87

0.87

0.91

0.89

Precision

0.92

0.83

0.90

0.90

0.78

0.78

0.84

0.82

Recall

0.82

0.92

0.87

0.80

1.00

1.00

1.00

1.00

Sensitivity

0.82

0.92

0.87

0.80

1.00

1.00

1.00

1.00

Specificity

0.85

0.55

0.75

0.85

0.00

0.00

0.29

0.14