Reduction of Feature Vectors Using Rough Set Theory for Detection of TB

P. Prasanna Kumari*, B. Prabhakara Rao**
*_**Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.
Periodicity:July - September'2019
DOI : https://doi.org/10.26634/jip.6.3.16698

Abstract

Tuberculosis (TB) is a global health problem and an infectious disease for people with low immunity and HIV/AIDS patients. Unfortunately diagnosing tuberculosis is still a major challenge. In any medical diagnosis, classifier plays an important role. In order to improve the speed of the classifier; it is required to use the selected features. To reduce the dimensionality of feature vector the authors have used RST based Multi Kernel Fuzzy C-Means (MKFCM) method for selecting features which are indispensable. Features those are not selected are treated as redundant or superfluous and are removed from the final feature vector. The performance of proposed methodology is analyzed in terms of accuracy, performance curve and regression plots. As a result, the suggested method gives the better performance.

Keywords

Tuberculosis, Curse of Dimensionality, Dimensionality Reduction, Feature Selection, Rough Set, Multi Kernel Fuzzy C-Means Rough Set theory (MKFCMRS).

How to Cite this Article?

Kumari, P. P., and Rao, B. P. (2019). Reduction of Feature Vectors Using Rough Set Theory for Detection of TB. i-manager's Journal on Image Processing, 6(3), 10-16. https://doi.org/10.26634/jip.6.3.16698

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