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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