Research Article
Drug-Drug Interactions Prediction Using Fingerprint Only
Table 3
Comparison of RF and SVM classifiers under the two types of tenfold cross-validation.
| | Cross-validation | Classification algorithm | Model | Accuracy | Precision | Recall | F1-measure | MCCa |
| | Entire tenfold cross-validation | Random forest | Addition + subtraction | 89.54% | 92.70% | 87.20% | 89.86% | 79.25% | | Support vector machine | 81.92% | 85.92% | 79.57% | 82.62% | 64.06% | | Composition tenfold cross-validation | ODITb test dataset | Random forest | Addition + Hadamard | 79.86% | 75.20% | 82.99% | 78.89% | 60.06% | | Support vector machine | 64.65% | 61.20% | 65.53% | 63.06% | 29.51% | | NDITc test dataset | Random forest | Addition + Hadamard | 64.49% | 41.56% | 76.82% | 53.73% | 32.64% | | Support vector machine | 57.45% | 48.26% | 58.41% | 52.46% | 15.07% |
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aMCC: Mathews correlation coefficient. bODIT: One Drug In Train set. cNDIT: No Drug In Train set.
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