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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. Attached Files Name Description MIMEType Size Downloads 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   Link to Related Work An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans (deposited 19-07-2014) Advanced Computer Science Applications and Technologies (ACSAT) Publisher (deposited 01-04-2014)   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 18 Mar 2014, 08:22:52 EST Tue, 18 Mar 2014, 09:50:55 EST Tue, 01 Apr 2014, 13:55:50 EST Wed, 09 Apr 2014, 15:35:46 EST Thu, 10 Apr 2014, 08:00:55 EST Thu, 10 Apr 2014, 11:57:02 EST Thu, 10 Apr 2014, 15:53:34 EST Thu, 10 Apr 2014, 15:55:03 EST Fri, 11 Apr 2014, 11:29:18 EST Filtered Full Access Statistics: 33 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Tue, 18 Mar 2014, 08:22:52 EST

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