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The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets
- Source :
- Applied Bionics and Biomechanics, Applied Bionics and Biomechanics, Vol 2020 (2020)
- Publication Year :
- 2020
-
Abstract
- Mirnezami, Alexander/0000-0002-6199-8332 WOS:000594274800001 PubMed: 33204304 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.
- Subjects :
- SELECTION
Article Subject
QH301-705.5
Computer science
Biomedical Engineering
Medicine (miscellaneous)
Bioengineering
02 engineering and technology
Minority class
Machine learning
computer.software_genre
Measure (mathematics)
03 medical and health sciences
0202 electrical engineering, electronic engineering, information engineering
Hellinger
Sensitivity (control systems)
Biology (General)
Hellinger distance
SMOTE
030304 developmental biology
0303 health sciences
business.industry
Medical Datasets
ALGORITHMS
Baseline model
Classification
Class (biology)
Majority class
Undersampling
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
TP248.13-248.65
Biotechnology
Research Article
Subjects
Details
- ISSN :
- 11762322
- Volume :
- 2020
- Database :
- OpenAIRE
- Journal :
- Applied bionics and biomechanics
- Accession number :
- edsair.doi.dedup.....146d48274cb8b245f60c4bbaad6b16dc