A hybrid feature selection with ensemble classification for imbalanced healthcare data: a case study for brain tumor diagnosis Huda, Shamsul, Yearwood, John, Jelinek, Herbert F., Hassan, Mohammad Mehedi, Fortino, Giancarlo and Buckland, Michael 2016, A hybrid feature selection with ensemble classification for imbalanced healthcare data: a case study for brain tumor diagnosis, IEEE Access, vol. 4, Special section on healthcare big data, pp. 9145-9154, doi: 10.1109/ACCESS.2016.2647238. Attached Files Name Description MIMEType Size Downloads huda-ahybridfeature-2016.pdf Published version application/pdf 5.92MB 21 Title A hybrid feature selection with ensemble classification for imbalanced healthcare data: a case study for brain tumor diagnosis Author(s) Huda, Shamsul Yearwood, John orcid.org/0000-0002-7562-6767 Jelinek, Herbert F. Hassan, Mohammad Mehedi Fortino, Giancarlo Buckland, Michael Journal name IEEE Access Volume number 4 Season Special section on healthcare big data Start page 9145 End page 9154 Total pages 10 Publisher IEEE Place of publication Piscataway, N.J. Publication date 2016 ISSN 2169-3536 Keyword(s) brain tumor morphological features ANNIGMA MRMR feature selection classification Science & Technology Technology Computer Science, Information Systems Engineering, Electrical & Electronic Telecommunications Computer Science Engineering OLIGODENDROGLIAL TUMORS FRACTAL DIMENSIONS NEURAL-NETWORKS SEGMENTATION IMAGES CLASSIFIERS FRAMEWORK MRI Summary Electronic health records (EHRs) are providing increased access to healthcare data that can be made available for advanced data analysis. This can be used by the healthcare professionals to make a more informed decision providing improved quality of care. However, due to the inherent heterogeneous and imbalanced characteristics of medical data from EHRs, data analysis task faces a big challenge. In this paper, we address the challenges of imbalanced medical data about a brain tumor diagnosis problem. Morphometric analysis of histopathological images is rapidly emerging as a valuable diagnostic tool for neuropathology. Oligodendroglioma is one type of brain tumor that has a good response to treatment provided the tumor subtype is recognized accurately. The genetic variant, 1p-/19q-, has recently been found to have high chemosensitivity, and has morphological attributes that may lend it to automated image analysis and histological processing and diagnosis. This paper aims to achieve a fast, affordable, and objective diagnosis ofthis genetic variant of oligodendroglioma with a novel data mining approach combining a feature selection and ensemble-based classification. In this paper, 63 instances of brain tumor with oligodendroglioma are obtained due to prevalence and incidence of the tumor variant. In order to minimize the effect of an imbalanced healthcare data set, a global optimization-based hybrid wrapper-filter feature selection with ensemble classification is applied. The experiment results show that the proposed approach outperforms the standard techniques used in brain tumor classification problem to overcome the imbalanced characteristics of medical data. Language eng DOI 10.1109/ACCESS.2016.2647238 Field of Research 080109 Pattern Recognition and Data Mining 090303 Biomedical Instrumentation 080699 Information Systems not elsewhere classified Socio Economic Objective 0 Not Applicable HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2017, IEEE Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30093913 Document type: Journal Article Collections: School of Information Technology School of Engineering Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 07 Apr 2017, 14:21:19 EST Sat, 08 Apr 2017, 05:07:12 EST Tue, 11 Apr 2017, 11:50:36 EST Wed, 12 Apr 2017, 06:08:27 EST Sat, 29 Apr 2017, 02:30:05 EST Mon, 22 May 2017, 12:23:41 EST Mon, 22 May 2017, 12:27:45 EST Mon, 22 May 2017, 15:33:30 EST Fri, 16 Jun 2017, 11:35:31 EST Filtered Full Citation counts:   Cited 5 times in TR Web of Science Cited 8 times in Scopus Search Google Scholar Access Statistics: 126 Abstract Views, 20 File Downloads  -  Detailed Statistics Created: Tue, 11 Apr 2017, 11:50:36 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.