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  1. University of Computer Studies, Yangon
  2. Conferences

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

http://hdl.handle.net/20.500.12678/0000003706
http://hdl.handle.net/20.500.12678/0000003706
3f0e9362-5763-4d21-b010-5732f421d960
0f87a893-f196-4f1e-8c45-181a8fe93c36
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54016.pdf 54016.pdf (378 Kb)
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Article
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Publication
Title
Title Evaluation of Symptoms in Heart Disease Patients by using k - Nearest Neighbor Classification
Language en
Publication date 2009-12-30
Authors
Maw, Hnin Yu
Sandar, Khin
Oo, May Phyo
Description
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.
Keywords
Machine learning, k - nearest neighbor classifier, classifier accuracy, k nearest neighbor algorithm, Coronary Artery Disease
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1444
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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