The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets
Joint Authors
Al-Shamaa, Zina Z. R.
Kurnaz, Sefer
Duru, Adil Deniz
Peppa, Nadia
Mirnezami, Alex H.
Hamady, Zaed Z. R.
Source
Applied Bionics and Biomechanics
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-04
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Imbalanced class distribution in the medical dataset is a challenging task that hinders classifying disease correctly.
It emerges when the number of healthy class instances being much larger than the disease class instances.
To solve this problem, we proposed undersampling the healthy class instances to improve disease class classification.
This model is named Hellinger Distance Undersampling (HDUS).
It employs the Hellinger Distance to measure the resemblance between majority class instance and its neighbouring minority class instances to separate classes effectively and boost the discrimination power for each class.
An extensive experiment has been conducted on four imbalanced medical datasets using three classifiers to compare HDUS with a baseline model and three state-of-the-art undersampling models.
The outcomes display that HDUS can perform better than other models in terms of sensitivity, F1 measure, and balanced accuracy.
American Psychological Association (APA)
Al-Shamaa, Zina Z. R.& Kurnaz, Sefer& Duru, Adil Deniz& Peppa, Nadia& Mirnezami, Alex H.& Hamady, Zaed Z. R.. 2020. The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets. Applied Bionics and Biomechanics،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1120159
Modern Language Association (MLA)
Al-Shamaa, Zina Z. R.…[et al.]. The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets. Applied Bionics and Biomechanics No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1120159
American Medical Association (AMA)
Al-Shamaa, Zina Z. R.& Kurnaz, Sefer& Duru, Adil Deniz& Peppa, Nadia& Mirnezami, Alex H.& Hamady, Zaed Z. R.. The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets. Applied Bionics and Biomechanics. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1120159
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1120159