Research Article
An Efficient Combination among sMRI, CSF, Cognitive Score, and APOE ε4 Biomarkers for Classification of AD and MCI Using Extreme Learning Machine
Table 7
Comparison of classification of AD vs. HC and HC vs. MCI between the proposed method and existing state-of-the-art methods.
| Author | Data | Classifier | Feature selection | AD vs. HC (%) | HC vs. MCI |
| Westman et al. [67] | MRI + CSF | OPLS | — | 91.8 | 77.6% | Johnson et al. [68] | MRI + PET + CSF + cognitive scores | Stacked autoencoder | Sparse representation learning | 95.9 | 85% | Hinrichs et al. [69] | MRI + PET + CSF + APOE + cognitive scores | MKL | — | 92.4 | n/a | Zhang and Shen et al. [39] | MRI + PET + CSF | SVM | Multitask feature selection | 93.3 | 83.2% | Beheshti et al. [70] | sMRI | SVM | Feature ranking + genetic algorithm | 93.01 | — | Spasov et al. [71] | sMRI + cognitive measures + APOE + demographic | CNN | — | 99.5 | — | Maqsood et al. [72] | sMRI | CNN | — | 92.85 | — | Proposed method | MRI + CSF + APOE + MMSE | ELM | Filter (MRMR) + wrapper (SFS) | 97.31 | 91.72% |
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