Reduct Based Rule Generation for Medical Application Using Rough Sets

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Renu Vashist
Prof. M.L Garg


Rough set theory is an important tool to intelligent data analysis. It is used for dealing with uncertainty in the hidden pattern of data. This paper outlines concepts of the rough set theory for finding decision rules. It assumes that the information about the real world is given in the form of an information table which represents input data, gathered from any domain, such as, medicine, financial markets, banking, etc.. In this study we have acquired the data from the medical science and framed some rules using concept of reduct to make the decision related to the heart problem of a patient. Application of intelligent methods in medical science is a very challenging issue and will be of utmost importance in the future. In this research paper basic ideas of rough set theory are presented with possible intelligent applications for medical case of heart patients.

Keywords Rough Set, Information System, Rule Generation, Reduct, Core, Lower Approximation, Upper Approximation, Decision Algorithm.


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