Research Article
Diagnosis of Alzheimer’s Disease with Extreme Learning Machine on Whole-Brain Functional Connectivity
Table 3
Comparison of classification performances with references.
| Reference | Data set AD : MCI:CN | Feature measures | Feature selection | Classifier | Accuracy (%) | AUC | AD vs. CN | MCI vs. CN | AD vs. CN | MCI vs. CN |
| [14] | -:25 : 25 | Pearson correlation of regional cortical thickness | t-test; mRMR; SVM-RFE; | SVM | 92.35 | 84 | 0.9744 | 0.9233 |
| [15] | -:12 : 25 | FC | t-test; SVM-RFE; | SVM | N/A | 91.9 | N/A | 0.94 |
| [27] | 25:-:36 | FC | Random neural network cluster | Elman neural network | 92.31 | N/A | N/A | N/A |
| [17] | 34 : 31 : 31 | ReHo; FC | SVM-RFE; LASSO; t-test | ELM | 98.86 | 98.57 | N/A | N/A |
| [18] | 118 : 118 : 118 | FC | Recurrent learning method; convolutional learning method; | ELM | N/A | N/A | 0.913 | 0.824 |
| [19] | 118 : 118 : 118 | FC | Select features by threshold | GNEA | N/A | N/A | 0.813 | 0.703 |
| [20] | 31 : 31 : 31 | FC, graph-embedding | SVM-RFE; LASSO; FSASL | LSVM | 90.63 | 97.8 | N/A | N/A | RELM | 93.86 | 98.91 | N/A | N/A |
| Proposed method | 100:100:100 | FC | None | Parallel ELM | 96.85 | 95.05 | 0.9891 | 0.9888 |
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