Table 5.
Performance comparison of the proposed and competing methods on the (UM + NYU) combined dataset
| Method | ACC (%) | SEN (%) | SPEC (%) | PPV (%) | NPV (%) | |
|---|---|---|---|---|---|---|
| Single cluster (sc) | SVM | 65.15 | 56.67 | 72.22 | 62.96 | 66.67 |
| RSVM | 57.58 | 53.33 | 61.11 | 53.33 | 61.11 | |
| GSVM | 62.88 | 50.00 | 73.61 | 61.22 | 63.86 | |
| DRBM | 64.39 | 58.33 | 69.44 | 61.40 | 66.67 | |
| Multiple cluster (mc) | SVM | 65.15 | 50.00 | 77.78 | 65.22 | 65.12 |
| RSVM | 64.39 | 53.33 | 73.61 | 62.75 | 65.43 | |
| GSVM | 65.91 | 56.67 | 73.61 | 64.15 | 67.09 | |
| DRBM | 67.42 | 58.33 | 75.00 | 66.04 | 68.35 | |
SVM: support vector machine; RSVM: recursive feature elimination‐based SVM; GSVM: graph theory‐based SVM; DRBM: discriminative restricted Boltzmann machine; ACC: accuracy; SEN: sensitivity; SPEC: specificity; PPV: positive predictive value; NPV: negative predictive value.