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Evaluation of Symptoms in Heart Disease Patients by using k - Nearest Neighbor Classification

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dc.contributor.author Maw, Hnin Yu
dc.contributor.author Sandar, Khin
dc.contributor.author Oo, May Phyo
dc.date.accessioned 2019-07-29T06:37:08Z
dc.date.available 2019-07-29T06:37:08Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1444
dc.description.abstract In many application domains, classification of complex measurements is essential in a diagnosis process. Correct classification of measurements may in fact be the most critical part of the diagnostic process. In this system, we intend to determine whether a patient has coronary artery disease (CAD) or not and if we have heart disease (CAD) what stage is it by using k - nearest neighbor classification. The k -nearest neighbor (k NN) is a sample and widely used technique which has found in several applications on classification problem. We can get classification accuracy by using k - nearest neighbor algorithm. Experiments were evaluated on some public datasets collected from the Cleveland Clinic Foundation in the UCI (University of California, Irvine) machine learning repository in order to test this system. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.subject Machine learning en_US
dc.subject k - nearest neighbor classifier en_US
dc.subject classifier accuracy en_US
dc.subject k nearest neighbor algorithm en_US
dc.subject Coronary Artery Disease en_US
dc.title Evaluation of Symptoms in Heart Disease Patients by using k - Nearest Neighbor Classification en_US
dc.type Article en_US


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