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Evaluation of feature selection algorithms for detection of depression from brain sMRI scans
conference contribution
posted on 2013-01-01, 00:00 authored by Kuryati Kipli, Abbas KouzaniAbbas Kouzani, Matthew JoordensMatthew JoordensDetection of depression from structural MRI (sMRI) scans is relatively new in the mental health diagnosis. Such detection requires processes including image acquisition and pre-processing, feature extraction and selection, and classification. Identification of a suitable feature selection (FS) algorithm will facilitate the enhancement of the detection accuracy by selection of important features. In the field of depression study, there are very limited works that evaluate feature selection algorithms for sMRI data. This paper investigates the performance of four algorithms for FS of volumetric attributes in sMRI scans. The algorithms are One Rule (OneR), Support Vector Machine (SVM), Information Gain (IG) and ReliefF. 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. The result of the evaluation of the FS algorithms is discussed by using a number of analyses.
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Complex Medical Engineering. International Conference (2013 : Beijing, China)Pagination
64 - 69Publisher
IEEELocation
Beijing, ChinaPlace of publication
Piscataway, N.J.Start date
2013-05-25End date
2013-05-28ISBN-13
9781467329705ISBN-10
1467329703Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
ICME 2013 : Proceedings of the International Conference on Complex Medical Engineering (CME)Usage metrics
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