Table 3.
Accuracies of classification of AD and NC using deep neural networks and nodal hub scores as features. * refers to a statistically significant result in comparison to all the others.
| Model | Accuracy |
|---|---|
| Features: Ambivert degree + Part. coeff. | |
| DNN (3 hidden layer of 50,32,10) | 79.4% |
| SVM (Linear, C = 0.1) | 66.5% |
| Features: (Ambivert degree, Part. coeff.) | |
| DNN (3 hidden layers of 50,32,10) | 81.2%* |
| SVM (Linear, C = 0.1) | 64.5% |
| Features: Ambivert degree | |
| DNN (3 hidden layers of 50, 20, 10) | 77.5% |
| SVM (Linear, C = 0.1) | 67.1% |
| Features: Part. coeff. | |
| DNN (3 hidden layers of 40,20,10) | 71.7% |
| SVM (Linear, C = 0.01) | 62.8% |
| Features: Functional connectivity W | |
| DNN (3 hidden layer sizes: 500, 50, 10) | 76.7% |