An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans

Kipli, Kuryati, Kouzani, Abbas and Xiang, Yong 2013, An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans, in Proceedings of the 2nd International Conference on Advanced Computer Science Applications and Technologies; ACSAT 2013, [The Conference], [Sarawak, Malaysia], pp. 1-4.

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Title An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans
Author(s) Kipli, Kuryati
Kouzani, Abbas
Xiang, Yong
Conference name Advanced Computer Science Applications and Technologies. Conference (2nd : 2013 : Sarawak, Malaysia)
Conference location Sarawak, Malaysia
Conference dates 22-24 Dec. 2013
Title of proceedings Proceedings of the 2nd International Conference on Advanced Computer Science Applications and Technologies; ACSAT 2013
Editor(s) [Unknown]
Publication date 2013
Conference series Advanced Computer Science Applications and Technologies International Conference
Start page 1
End page 4
Total pages 4
Publisher [The Conference]
Place of publication [Sarawak, Malaysia]
Keyword(s) structural MRI
automated depression detection
classification
brain image analysis
Summary To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed individual from a healthy individual at individual scans level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification. It determines the class to which a sample belongs (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of its features. Thus far, very limited works have been reported for identification of a suitable classification algorithm for depression detection. In this paper, different types of classification algorithms are compared for effective diagnosis of depression. Ten independent classification schemas are applied and a comparative study is carried out. The algorithms are: Naïve Bayes, Support Vector Machines (SVM) with Radial Basis Function (RBF), SVM Sigmoid, J48, Random Forest, Random Tree, Voting Feature Intervals (VFI), LogitBoost, Simple KMeans Classification Via Clustering (KMeans) and Classification Via Clustering Expectation Minimization (EM) respectively. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. A classification accuracy evaluation method was employed for evaluation and comparison of the performance of the examined classifiers.
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 E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30061615

Document type: Conference Paper
Collections: School of Information Technology
School of Engineering
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