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Accuracy Comparison of Inlier Method and Random Row Method (Heart-disease)

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dc.contributor.author Aye, Nan Saung Chan
dc.contributor.author Aye, Nilar
dc.date.accessioned 2019-07-19T13:11:44Z
dc.date.available 2019-07-19T13:11:44Z
dc.date.issued 2017-12-27
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1071
dc.description.abstract Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease patients. Decision Tree is one of the data mining techniques used in the diagnosis of heart disease showing considerable success. Kmeans clustering is one of the most popular clustering techniques; however initial centroid selection strongly affects its results.In this paper, heart-disease dataset is considered for study.The implemented system will be useful to find out the patient’s level in the heart-diseases. This paper implements integrating initial centroid selection of the k-means clustering such as inlier and random row methods with decision tree in the diagnosis of heart disease patients. The result shows that integrating k-means clustering with decision tree with initial centroid selection could enhance the accuracy in diagnosing heart disease patients. It also shows that the inlier initial centroid selection method could achieve higher accuracy than random row initial centroid selection methods in the diagnosis of heart disease patients. en_US
dc.language.iso en en_US
dc.publisher Eighth Local Conference on Parallel and Soft Computing en_US
dc.title Accuracy Comparison of Inlier Method and Random Row Method (Heart-disease) en_US
dc.type Article en_US


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