Table 1 The results of the classifiers for single-label, multi-class dataset
From: Deep learning architectures for multi-label classification of intelligent health risk prediction
Algorithm | Accuracy (%) | Precision | Recall | F-Score |
|---|---|---|---|---|
 LibSVM | 49.89 | 0.422 | 0.499 | 0.416 |
 MLP | 74.94 | 0.744 | 0.749 | 0.744 |
 SMO | 69.67 | 0.691 | 0.697 | 0.670 |
 J48 | 77.26 | 0.771 | 0.773 | 0.771 |
 DNN | 71.10 | 0.757 | 0.711 | 0.726 |
 RF | 81.51 | 0.810 | 0.815 | 0.808 |