Table 3 Classification performance using all measures.
Study and description | Classifier(s) | Parameters tuned | Accuracy | Sensitivity | Specificity | F1 | AUC |
|---|---|---|---|---|---|---|---|
6—All subcortical and cortical measures | Logistic Regression | Regularization parameter C, Max iterations, penalty, solver | 77% | 79% | 74% | 0.83 | 0.77 |
7—Ensemble with 3 inputs (a) SV (b) CV (c) CA + CT + CMC | Ensemble—Hard Voting (a) Support Vector Classifier (b) Nu-Support Vector Classifier (c) Logistic Regression | 83% | 90% | 70% | 0.83 | 0.80 | |
8—Ensemble with 5 inputs (a) SV (b) CV (c) CA (d) CT € CMC | Ensemble—Soft Stacking (a) Support Vector Classifier (b) Nu-Support Vector Classifier (c) Support Vector Classifier (d) Support Vector Classifier (e) Logistic Regression | Regularization parameter C, Max iterations, penalty, solver | 87% | 98% | 65% | 0.87 | 0.82 |
