Table 10 Comparison of MCInc/MCIc classification accuracy in literature.
Method | Subjects (MCInc/MCIc) | Data source | Features | Classifier | ACC (%) | SEN (%) | SPE (%) |
|---|---|---|---|---|---|---|---|
Korolev et al.41 | 120/139 (baseline visit) | Risk factors, cognitive and functional assessments, MRI, plasma proteomic data | ROI-wise | Probabilistic multiple kernel learning | 80.0 | 83.0 | 76.0 |
Tang et al.39 | 87/135 (baseline visit) | MRI | Vertex-based | LDA | 75.0 | 77.0 | 71.0 |
Liu et al.30 | 128/ 76 (baseline visit) | MRI | Voxel-wise | Hierarchical ensemble | 64.8 | 22.2 | 89.6 |
Suk et al.16 | 128/76 (baseline visit) | MRI, PET | Voxel-wise | Hierarchical ensemble | 75.9 | 48.0 | 95.2 |
Wee et al.40 | 111/89 (baseline visit) | MRI | Vertex-based | SVM | 75.1 | 63.5 | 84.4 |
Zhang et al.3 | 50/35 (longitudinal data) | MRI, PET, cognitive scores | ROI-wise | SVM | 78.4 | 79.0 | 78.0 |
Proposed method | 61/70 (longitudinal data) | MRI | Voxel-wise | Hierarchical ensemble | 79.4 | 86.5 | 78.2 |
