Research Article
Predicting Breast Cancer Based on Optimized Deep Learning Approach
Table 8
The performance of applying regular ML models and DL model with selected features by REF.
| Approaches | Models | CV performance | Testing performance | AC | PR | RE | FM | AC | PR | RE | FM |
| Regular ML approach | DT | 94.24 | 94.48 | 94.24 | 94.4 | 88.82 | 89.14 | 88.82 | 88.89 | KNN | 89.85 | 90.63 | 89.85 | 89.51 | 86.67 | 87.4 | 86.67 | 86.17 | NB | 80.74 | 81.22 | 80.74 | 79.85 | 83.51 | 84.09 | 83.51 | 82.84 | RF | 96.57 | 96.72 | 96.48 | 96.45 | 93.86 | 93.86 | 93.86 | 93.86 | SVM | 93.74 | 93.98 | 93.74 | 93.68 | 93.86 | 93.85 | 93.86 | 93.84 |
| DL approach | The optimized deep RNN | 97.92 | 97.77 | 97.79 | 97.78 | 95.18 | 95.44 | 95.18 | 95.21 |
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