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Prediction of Significant Heart Attack Patterns Using Clustering Algorithm

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dc.contributor.author Han, Aye Mya
dc.date.accessioned 2019-08-04T17:35:02Z
dc.date.available 2019-08-04T17:35:02Z
dc.date.issued 2009-12-30
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1713
dc.description.abstract This system presents an efficient approach for discovering significant patterns from the heart disease database for heart attack prediction. The heart disease data warehouse is clustered using Kmeans clustering algorithm to extract related data. The primary intent of the system is to design and develop an efficient approach for extracting patterns, which are significant to heart attack, from the heart disease database. The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various factors or symptoms is a multi-layered issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. The proposed system aims to utilize the data mining techniques: clustering and frequent pattern mining. en_US
dc.language.iso en en_US
dc.publisher Fourth Local Conference on Parallel and Soft Computing en_US
dc.subject Clustering Algorithm en_US
dc.subject Heart Attack en_US
dc.subject Frequent pattern mining en_US
dc.subject Data Mining en_US
dc.subject Disease Diagnosis en_US
dc.subject Heart Disease en_US
dc.subject Pre-processing en_US
dc.subject Frequent Patterns en_US
dc.subject MAFIA (MAximal Frequent Itemset Algorithm) en_US
dc.subject Clustering en_US
dc.subject K-Means en_US
dc.subject Significant Patterns en_US
dc.title Prediction of Significant Heart Attack Patterns Using Clustering Algorithm en_US
dc.type Article en_US


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