Frequent Pattern Bayesian Classification for ECG Pattern Diagnosis 


Vol. 11,  No. 5, pp. 1031-1040, Oct.  2004
10.3745/KIPSTD.2004.11.5.1031


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  Abstract

Electrocardiogram being the recording of the heart's electrical activity provides valuable clinical information about heart's status. Many researches 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.

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[IEEE Style]

G. Y. Noh, W. S. Kim, H. G. Lee, S. T. Lee, K. H. Ryu, "Frequent Pattern Bayesian Classification for ECG Pattern Diagnosis," The KIPS Transactions:PartD, vol. 11, no. 5, pp. 1031-1040, 2004. DOI: 10.3745/KIPSTD.2004.11.5.1031.

[ACM Style]

Gi Yeong Noh, Wuon Shik Kim, Hun Gyu Lee, Sang Tae Lee, and Keun Ho Ryu. 2004. Frequent Pattern Bayesian Classification for ECG Pattern Diagnosis. The KIPS Transactions:PartD, 11, 5, (2004), 1031-1040. DOI: 10.3745/KIPSTD.2004.11.5.1031.

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