You are not logged in.
Openly accessible

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 13

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
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

Connect to link resolver
 
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
Citation counts: TR Web of Science Citation Count  Cited 2 times in TR Web of Science
Scopus Citation Count Cited 2 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 62 Abstract Views, 13 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.