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Table 2 The results of the classifiers for multi-label dataset

From: Deep learning architectures for multi-label classification of intelligent health risk prediction

Base Classifier

Accuracy (%)

Precision

Recall

F-Score

 RAkEL-LibSVM

59.47

0.697

0.603

0.630

 RAkEL-MLP

81.63

0.854

0.838

0.837

 RAkEL-SMO

59.47

0.697

0.603

0.630

 RAkEL-J48

83.64

0.864

0.865

0.856

 RAkEL-RF

85.67

0.884

0.880

0.874

 MLkNN

51.03

0.602

0.530

0.547

 DNN

92.07

0.915

0.867

0.823

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BMC Bioinformatics

ISSN: 1471-2105

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