Rough Set Based Rule Generation Techniques in Medical Diagnosis: With Reference to Identification of Heart Disease

Pragati Jain¹ , Kamini Agrawal² , Deepensha Vaishnav³

Section:Research Paper, Product Type: Isroset-Journal
Vol.4 , Issue.3 , pp.12-18, Jun-2017

Online published on Jun 30, 2017

Copyright © Pragati Jain, Kamini Agrawal, Deepensha Vaishnav . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Rough Set Theory proposed by Pawlak in 1982 has now become very significant in the field of data mining and knowledge discovery. This led to his most widely recognized contribution to classifying objects with their attributes and his introduction of approximation spaces, which establish the foundation of granular computing and provide framework for perception and knowledge discovery in many areas. The theory of rough sets has been under continuous development and a fast growing group of researchers and practitioners are interested in this methodology. The theory has found many interesting applications in medicine, pharmacology, business, banking, market research, engineering design, meteorology, vibration analysis, switching function, conflict analysis, image processing, voice recognition, concurrent system analysis, decision analysis, character recognition, and other fields. The paper presents the concept of Rough Set Theory along with the Rule Generation Techniques in Medical Diagnosis in general and particularly for the identification of heart disease through the Core and Reduct of the concerned attributes of an information system.

Key-Words / Index Term :
Rough Set Theory, Data Mining, Heart Disease, QRA, LEM2 Algorithm

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