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Extraction of Frequent Patterns from Diabetes Cluster

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dc.contributor.author Win, Myint Swe Lai
dc.contributor.author Phyu, Win Lei Lei
dc.date.accessioned 2019-07-15T08:06:41Z
dc.date.available 2019-07-15T08:06:41Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/901
dc.description.abstract Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and significant structures, from large amounts of data stored in databases, data warehouses, or other information repositories. In this paper, we have proposed an efficient approach for the extraction of significant patterns from the patients database for diabetes prediction. The diagnosis of diseases is a significant and tedious task in medicine. To facilitate the diagnosis process, the effort to utilize knowledge and experience of numerous specialists and clinical screening data of patients collected in databases is considered a valuable option. The patients database is clustered using the KMIX clustering algorithm, which will extract the data relevant to diabetes from the database. Subsequently the frequent patterns are mined from the extracted data, relevant to diabetes, using the MAFIA algorithm. Then the significant patterns to diabetes diagnosis are chosen from these frequent patterns. These patterns can be used to apply in the healthcare system. en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.subject Data Mining en_US
dc.subject Diabetes Diagnosis en_US
dc.subject Clustering en_US
dc.subject KMIX en_US
dc.subject Frequent Pattern Mining en_US
dc.subject MAFIA en_US
dc.subject Significant Pattern en_US
dc.title Extraction of Frequent Patterns from Diabetes Cluster en_US
dc.type Article en_US

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