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An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans
conference contribution
posted on 2013-01-01, 00:00 authored by Kuryati Kipli, Abbas KouzaniAbbas Kouzani, Yong XiangYong XiangTo 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.
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Event
Advanced Computer Science Applications and Technologies. Conference (2nd : 2013 : Sarawak, Malaysia)Pagination
333 - 336Publisher
IEEE Computer SocietyLocation
Sarawak, MalaysiaPlace of publication
[Sarawak, Malaysia]Publisher DOI
Start date
2013-12-22End date
2013-12-24Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
ACSAT 2013 : Proceedings of the 2nd International Conference on Advanced Computer Science Applications and Technologies. 2013Usage metrics
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