Heart Disease Detection using EKSTRAP Clustering with Statistical and Distance based Classifiers
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 3
Abstract
Abstract : The heart is the most important organ in the human body which pumps blood to various parts of the body. If there is inefficient circulation of blood in body organs like brain will suffer. If heart stops pumping blood it results in death. An individual’s life is very much dependent on how efficiently the heart works. Usingdata mining technique proposed in this paper we are trying to detect if a patient has heart disease or not. The system uses 13 attributes like age, gender, blood pressure, cholesterol etc to detect the same. The system uses a hybrid technique which uses Enhanced K STRAnge Points(EKSTRAP) clustering algorithm , output of which is given to different classifiers like statistical –Naïve Bayes classifier and Distance Based – MSDC (Modified Simple Distance Classifier ).
Authors and Affiliations
Terence Johnson , Dr. S. K. Singh , Vaishnavi Kamat , Aishwarya Joshi , Lester D‟Souza , Poohar Amonkar , Devyani Joshi , Anirudha Kulkarni
Keywords
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- EP ID EP164551
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