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Prediction of Significant Heart Attack Patterns Using Clustering Algorithm
http://hdl.handle.net/20.500.12678/0000004001
http://hdl.handle.net/20.500.12678/000000400141956da3-5ee8-4a1c-8b9e-c7ab5e7bed2c
452d2332-233c-4409-a928-125664350d42
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55108.pdf (906 Kb)
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Article | ||||||
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Publication | ||||||
Title | ||||||
Title | Prediction of Significant Heart Attack Patterns Using Clustering Algorithm | |||||
Language | en | |||||
Publication date | 2009-12-30 | |||||
Authors | ||||||
Han, Aye Mya | ||||||
Description | ||||||
This system presents an efficient approachfor discovering significant patterns from the heartdisease database for heart attack prediction. Theheart disease data warehouse is clustered using Kmeansclustering algorithm to extract related data.The primary intent of the system is to design anddevelop an efficient approach for extractingpatterns, which are significant to heart attack, fromthe heart disease database. The diagnosis ofdiseases is a significant and tedious task inmedicine. The detection of heart disease fromvarious factors or symptoms is a multi-layeredissue which is not free from false presumptionsoften accompanied by unpredictable effects. Thusthe effort to utilize knowledge and experience ofnumerous specialists and clinical screening data ofpatients collected in databases to facilitate thediagnosis process is considered a valuable option.The proposed system aims to utilize the data miningtechniques: clustering and frequent pattern mining. | ||||||
Keywords | ||||||
Clustering Algorithm, Heart Attack, Frequent pattern mining, Data Mining, Disease Diagnosis, Heart Disease, Pre-processing, Frequent Patterns, MAFIA (MAximal Frequent Itemset Algorithm), Clustering, K-Means, Significant Patterns | ||||||
Identifier | http://onlineresource.ucsy.edu.mm/handle/123456789/1713 | |||||
Journal articles | ||||||
Fourth Local Conference on Parallel and Soft Computing | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |