The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets

Applied Bionics and Biomechanics
Zina Z R Al-ShamaaZaed Z R Hamady

Abstract

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.

References

Aug 8, 2012·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Nicolás García-PedrajasAida de Haro-García
Mar 26, 2013·BMC Bioinformatics·Rok Blagus, Lara Lusa
Jan 7, 2017·PloS One·Min-Wei HuangChih-Fong Tsai

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Software Mentioned

SMOTE
Tomek
HDUS
Python language
ROS
spyder

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