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Rough Set Approach for Generation of Classification Rules for Hepatitis

Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan, Radhanath Hota Published in Artificial Intelligence

Communications on Applied Electronics
Year of Publication: 2015
© 2015 by CAE Journal

Sujogya Mishra, Shakti Prasad Mohanty, Sateesh Kumar Pradhan and Radhanath Hota. Article: Rough Set Approach for Generation of Classification Rules for Hepatitis. Communications on Applied Electronics 2(2):22-27, June 2015. Published by Foundation of Computer Science, New York, USA. BibTeX

	author = {Sujogya Mishra and Shakti Prasad Mohanty and Sateesh Kumar Pradhan and Radhanath Hota},
	title = {Article: Rough Set Approach for Generation of Classification Rules for Hepatitis},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {2},
	number = {2},
	pages = {22-27},
	month = {June},
	note = {Published by Foundation of Computer Science, New York, USA}


In the current age research in the field of medical science has been increased to a significant height but there are several new virus which cannot be detect by the usual medical test , for example some common disease like malaria ,dengue, hepatitis , jaundice needs of very meticulous medical analysis because all the above said dieses has very common symptoms which needs of strong analysis to determine the exact dieses. Maximum number of medical test which are conducted to determine the dieses mostly based upon doctor's guess which are not only expensive and but also give inaccurate pathological result. In this paper we emphasized more on symptom rather than pathological test . From the large domain we consider the disease hepatitis for our purpose . Every year millions of people died from hepatitis due to improper diagnosis . We develop an algorithm using rough set concept to counter hepatitis. We classified the entire paper in to three basic section 1st section about literature review the 2nd and 3rd section deals with the Experiment, Findings, and Statistical Validation.


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Rough Set Theory, Medical related data, Granular computing, Data mining.