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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 Xiang
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.



Advanced Computer Science Applications and Technologies. Conference (2nd : 2013 : Sarawak, Malaysia)


333 - 336


IEEE Computer Society


Sarawak, Malaysia

Place of publication

[Sarawak, Malaysia]

Start date


End date




Publication classification

E1 Full written paper - refereed

Copyright notice

2013, IEEE

Title of proceedings

ACSAT 2013 : Proceedings of the 2nd International Conference on Advanced Computer Science Applications and Technologies. 2013