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

<div><i>Average weight of different types of feature.</i> Statistical analysis of the average weight for subgraph pattern features, minimum spanning tree of quantifiable local network features, and features used in this study. The average weight of subgraph pattern features was 550.31, the minimum spanning tree of quantifiable local network features was 915.42, and that for both feature types together was 945.16. The combination of the two different types of features had the greatest weight.</div>

Computational and Mathematical Methods in Medicine

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Figure 3: Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network 

Figure 3 | Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network 