Evolutional Diagnostic Rules Mining for Heart Disease Classification Using ECG Signal Data

Medical information related data sets are useful for the diagnosis and treatment of disease. With the development of technology and devices in biomedical engineering, it leads data overflow nowadays. Traditional data mining methods like SVM, ANN and decision tree are applied to perform the classification of arrhythmia disease. However, traditional analysis methods are far beyond the capacity and speed to deal with large scale of information. Techniques that have capability to handle the coming data sets in incremental learning phase can solve those problems. Therefore, in this paper, we proposed an incremental decision trees induction method which uses ensemble method for mining evolutional diagnostic rules for cardiac arrhythmia classification. Experimental results show that our proposed method performs better than other algorithms in our study.

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