Accuracy of automated classification of major depressive disorder as a function of symptom severity

Accuracy of automated classification of major depressive disorder as a function of symptom severity

Background: Growing evidence documents the potential of machine learning for developing brain based diagnostic methods for major depressive disorder (MDD). As symptom severity may influence brain activity, we investigated whether the severity of MDD affected the accuracies of machine learned MDD-vs-...

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Bibliographic Details
Main Authors: Rajamannar Ramasubbu, MD, FRCPC, MSc, Matthew R.G. Brown, PhD, Filmeno Cortese, MSc, Ismael Gaxiola, MSc, Bradley Goodyear, PhD, Andrew J. Greenshaw, PhD, Serdar M. Dursun, MD, PhD, Russell Greiner, PhD
Format: Article
Language:English
Published: Elsevier 2016-01-01
Series:NeuroImage: Clinical
Subjects:
Major depression
Severity of symptoms
Diagnosis
Functional magnetic resonance imaging
Machine learning
Classification
Support vector machine
Online Access:http://www.sciencedirect.com/science/article/pii/S2213158216301322

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