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Investigating machine learning techniques for detection of depression using structural MRI volumetric features

Kipli, Kuryati, Kouzani, Abbas Z. and Hamid, Isredza Rahmi A. 2013, Investigating machine learning techniques for detection of depression using structural MRI volumetric features, International journal of bioscience, biochemistry and bioinformatics, vol. 3, no. 5, pp. 444-448.

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Title Investigating machine learning techniques for detection of depression using structural MRI volumetric features
Author(s) Kipli, Kuryati
Kouzani, Abbas Z.
Hamid, Isredza Rahmi A.
Journal name International journal of bioscience, biochemistry and bioinformatics
Volume number 3
Issue number 5
Start page 444
End page 448
Total pages 5
Publisher International Association of Computer Science and Information Technology Press (IACSIT Press)
Place of publication Singapore
Publication date 2013-09
ISSN 2010-3638
Keyword(s) MRI
brain image analysis
image feature selection
machine learning
depression detection
Summary Structural MRI offers anatomical details and high sensitivity to pathological changes. It can demonstrate certain patterns of brain changes present at a structural level. Research to date has shown that volumetric analysis of brain regions has importance in depression detection. However, such analysis has had very minimal use in depression detection studies at individual level. Optimally combining various brain volumetric features/attributes, and summarizing the data into a distinctive set of variables remain difficult. This study investigates machine learning algorithms that automatically identify relevant data attributes for depression detection. Different machine learning techniques are studied for depression classification based on attributes extracted from structural MRI (sMRI) data. The attributes include volume calculated from whole brain, white matter, grey matter and hippocampus. Attributes subset selection is performed aiming to remove redundant attributes using three filtering methods and one hybrid method, in combination with ranker search algorithms. The highest average classification accuracy, obtained by using a combination of both SVM-EM and IG-Random Tree algorithms, is 85.23%. The classification approach implemented in this study can achieve higher accuracy than most reported studies using sMRI data, specifically for detection of depression.
Language eng
Field of Research 090399 Biomedical Engineering not elsewhere classified
080109 Pattern Recognition and Data Mining
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, IACSIT Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30061700

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.