Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

<div><i>Classification accuracy of different optimal parameters.</i> Optimal parameters were selected from a range of 0-1; step size, 0.1. Accuracy of classification with different optimal parameters was 94%–97%. Classification accuracy was greatest (97.54%) when the optimal parameter was 0.4.</div>

Computational and Mathematical Methods in Medicine

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Figure 5

Figure 5: Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network 

Figure 5 | Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network 