Table 3.
Results of the different machine learning methods to classify responders vs. nonresponders after the fourth cycle.
| Machine Learning Methods | ACC (%) | Sens (%) | Spec (%) | AUC (%) | N Features |
|---|---|---|---|---|---|
| PRIMARY ENDPOINT—migraine days reduction | |||||
| Random forest | 100 | 100 | 100 | 16,67 | 12 |
| SVM (linear kernel) | 76.67 | 63.33 | 90 | 23.33 | 3 |
| SVM (RBF kernel) | 76.67 | 70 | 83.33 | 23.33 | 2 |
| ANFIS (aNN) | 50 | 100 | 0 | 20 | 1 |
| MLP (aNN) | 85 | 90 | 80 | 15 | 2 |
| Fuzzy clustering (unsup. ML) | 45 | 90 | 0 | 86.67 | 2 |
| SECONDARY ENDPOINT—abortive medication intake reduction | |||||
| Random forest | 100 | 100 | 100 | 33 | 8 |
| SVM (linear kernel) | 83.33 | 76,67 | 90 | 16.67 | 4 |
| SVM (RBF kernel) | 83.33 | 83033 | 83.33 | 16.67 | 5 |
| ANFIS (aNN) | 50 | 100 | 0 | 21.67 | 1 |
| MLP (aNN) | 81.67 | 83.33 | 80 | 18.33 | 3 |
| Fuzzy clustering (unsup. ML) | 48.33 | 96.67 | 0 | 56.67 | 4 |
| SECONDARY ENDPOINT—reduction in days in which an abortive medication is required | |||||
| Random forest | 100 | 100 | 100 | 12.12 | 12 |
| SVM (linear kernel) | 88.33 | 76.67 | 100 | 11.67 | 5 |
| SVM (RBF kernel) | 75 | 90 | 60 | 25 | 3 |
| ANFIS (aNN) * | - | - | - | - | - |
| MLP (aNN) | 88.33 | 80 | 86.67 | 16.67 | 4 |
| Fuzzy clustering (unsup. ML) | 50 | 100 | 0 | 50 | 1 |
| EXPLORATORY ENDPOINT—MIDAS reduction | |||||
| Random forest | 100 | 100 | 100 | 27.27 | 2 |
| SVM (linear kernel) | 85 | 80 | 90 | 15 | 3 |
| SVM (RBF kernel) | 67.5 | 55 | 80 | 32.5 | 1 |
| ANFIS (aNN) * | - | - | - | - | - |
| MLP (aNN) | 85 | 85 | 85 | 15 | 2 |
| Fuzzy clustering (unsup. ML) | 47.5 | 95 | 0 | 57.5 | 1 |
Table: The population is composed of only those patients who completed 1 year of treatment. The comparison is performed between group 2 vs. 4 + 5. Legend: accuracy (ACC); sensibility (Sens); specificity (Spec); area under the curve (AUC); the number of features used for classification (N); suppor vector machine (SVM); RBF kernel); ANFIS (aNN); MLP (aNN); unsupervised (unsup.); * performance failure in predicting the outcome. Data are expressed as percentage (%).