Decision Making Using Rough Topology and Indiscernibility Matrix for Diagnosing Disease

Authors(2) :-Kaarthiga S, K. Sangeetha

Rough set theory has provided the necessary formalism and ideas for the development of some propositional machine learning systems. An important feature of rough sets is that the theory is followed by practical implementations of toolkits that support interactive model development. The main objective of this paper is to introduce and analyze the Rough Set Theory and also to decide the factors for diseases by using Indiscernibility and Boolean law.

Authors and Affiliations

Kaarthiga S
M.Sc., Mathematics, Department of Mathematics, Dr. SNS Rajalakshmi College of Arts and Science (Autonomous), Coimbatore, Tamilnadu, India
K. Sangeetha
Assistant Professor, Department of Mathematics, Dr. SNS Rajalakshmi College of Arts and Science (Autonomous), Coimbatore, Tamilnadu, India

Rough Sets, Set Approximation, Equivalence Class, Basis, Indiscernibility Matrix.

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Publication Details

Published in : Volume 4 | Issue 10 | September-October 2018
Date of Publication : 2018-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 135-138
Manuscript Number : IJSRSET1841021
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Kaarthiga S, K. Sangeetha, " Decision Making Using Rough Topology and Indiscernibility Matrix for Diagnosing Disease, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 10, pp.135-138, September-October.2018
URL : http://ijsrset.com/IJSRSET1841021

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