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Table 5 Comparison of results of different classifier models on the same data set

From: An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network

Classifier

ACC (%)

SEN (%)

SPE (%)

PRE (%)

MCC (%)

AUC (%)

Adaboost

70.82 ± 0.35

71.30 ± 1.15

70.34 ± 0.88

70.62 ± 0.41

41.65 ± 0.71

78.05 ± 0.52

Logistic

72.95 ± 0.45

72.98 ± 0.99

72.92 ± 0.68

72.94 ± 0.44

45.91 ± 0.91

80.41 ± 0.54

Naïve Bayes

68.27 ± 0.55

70.86 ± 0.86

65.69 ± 0.76

67.37 ± 0.53

36.60 ± 1.10

74.18 ± 0.62

Random forest

79.84 ± 0.50

80.03 ± 0.95

79.64 ± 0.22

79.72 ± 0.28

59.68 ± 1.00

87.90 ± 0.54

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BMC Bioinformatics

ISSN: 1471-2105

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