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http://dx.doi.org/10.3745/KIPSTD.2004.11D.5.1031

Frequent Pattern Bayesian Classification for ECG Pattern Diagnosis  

Noh, Gi-Yeong (한국표준과학연구원)
Kim, Wuon-Shik (한국표준과학연구원)
Lee, Hun-Gyu (한국표준과학연구원)
Lee, Sang-Tae (한국표준과학연구원)
Ryu, Keun-Ho (충북대학교 전기전자 및 컴퓨터공학부)
Publication Information
The KIPS Transactions:PartD / v.11D, no.5, 2004 , pp. 1031-1040 More about this Journal
Abstract
Electrocardiogram being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many re-searches have been pursued for heart disease diagnosis using ECG so far. However, electrocardio-graph uses foreign diagnosis algorithm due to inaccuracy of diagnosis results for a heart disease. This paper suggests ECG data collection, data preprocessing and heart disease pattern classification using data mining. This classification technique is the FB(Frequent pattern Bayesian) classifier and is a combination of two data mining problems, naive bayesian and frequent pattern mining. FB uses Product Approximation construction that uses the discovered frequent patterns. Therefore, this method overcomes weakness of naive bayesian which makes the assumption of class conditional independence.
Keywords
Data Mining; Frequent Pattern Bayesian Classification; Frequent Pattern Mining; Bayesian Classification; ECG Pattern Diagnosis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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