Table 5.
Performance comparison with different percentage split for dataset #1
| Technology | Accuracy (in %) | Sensitivity (in %) | Specificity (in %) | MCC | Precision | F1 Score | Gmean |
|---|---|---|---|---|---|---|---|
| 90% training set − 10% testing set | |||||||
| MLP-NN | 95 | 83.33 | 100 | 0.8819 | 1 | 0.9091 | 0.9129 |
| RBF-NN | 85 | 66.67 | 92.86 | 0.6299 | 0.8 | 0.7273 | 0.7868 |
| RNN | 90 | 87.5 | 100 | 0.7638 | 1.0 | 0.9333 | 0.9354 |
| LSTM | 85 | 81.25 | 100 | 0.6813 | 1.0 | 0.8965 | 0.9014 |
| SEFRON [Dataset#1] | 100 | 100 | 100 | 1 | 1 | 1 | 1 |
| 85% training set − 15% testing set | |||||||
| MLP-NN | 90 | 83.33 | 91.67 | 0.7092 | 0.7143 | 0.7692 | 0.8740 |
| RBF-NN | 90 | 66.67 | 95.83 | 0.6708 | 0.8 | 0.7273 | 0.7993 |
| RNN | 88.7 | 66 | 100 | 0.7512 | 1.0 | 0.7911 | 0.812 |
| LSTM | 89.72 | 100 | 84.3 | 0.808 | 0.7767 | 0.8743 | 0.92 |
| SEFRON [Dataset#1] | 93.33 | 100 | 91.67 | 0.8292 | 0.75 | 0.8571 | 0.9574 |
| 80% training set − 20% testing set | |||||||
| MLP-NN | 92.31 | 87.5 | 93.55 | 0.7767 | 0.7778 | 0.8235 | 0.9047 |
| RBF-NN | 89.74 | 62.5 | 96.77 | 0.6633 | 0.8333 | 0.7143 | 0.7777 |
| RNN | 92.86 | 100 | 90.11 | 0.85 | 0.80 | 0.89 | 0.95 |
| LSTM | 90 | 74.5 | 94.24 | 0.9274 | 1.0 | 0.9836 | 0.9837 |
| SEFRON [Dataset#1] | 92.31 | 87.5 | 93.55 | 0.7767 | 0.7778 | 0.8235 | 0.9047 |
| 70% training set − 30% testing set | |||||||
| MLP-NN | 93.22 | 84.62 | 95.65 | 0.8027 | 0.8462 | 0.8462 | 0.8996 |
| RBF-NN | 84.74 | 53.85 | 93.47 | 0.5223 | 0.7 | 0.6087 | 0.71 |
| RNN | 93.22 | 93.61 | 91.67 | 0.8068 | 0.9778 | 0.9565 | 0.9263 |
| LSTM | 93.22 | 93.61 | 91.67 | 0.8068 | 0.9778 | 0.9565 | 0.9263 |
| SEFRON [Dataset#1] | 89.83 | 100 | 86.96 | 0.7713 | 0.6842 | 0.8125 | 0.9325 |