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Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine

Please always quote using this URN: urn:nbn:de:bvb:20-opus-141412
  • Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would bePattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers.show moreshow less

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Metadaten
Author: Janaina Mourão-Miranda, David R. Hardoon, Tim Hahn, Andre F. Marquand, Steve C.R. Williams, John Shawe-Taylor, Michael Brammer
URN:urn:nbn:de:bvb:20-opus-141412
Document Type:Journal article
Faculties:Medizinische Fakultät
Language:English
Parent Title (English):NeuroImage
Year of Completion:2011
Volume:58
Issue:3
Pagenumber:793-804
Source:NeuroImage 58 (2011) 793–804
DOI:https://doi.org/10.1016/j.neuroimage.2011.06.042
Dewey Decimal Classification:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
Tag:Depression; Machine learning; Outlier detection; Pattern classification; Support Vector Machine; fMRI
Release Date:2019/06/25
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung