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Classification of Arrhythmia

Saleha Samad, Shoab A. Khan, Anam Haq, and Amna Riaz
National University of Sciences and Technology, College of E&ME, Rawalpindi, Pakistan
Abstract—The electrocardiogram (ECG) signal has great importance in diagnosing cardiac arrhythmias. In this paper we have compared three classifiers on the basis of their accuracies for the detection of arrhythmia. The algorithms that are used for classification are supervised machine learning algorithm. The performance of the classifier depends upon its accuracy rate. The classifiers used are Nearest Neighbors, Naive Bayes’, and Decision Tree classifier. The dataset used is publically available on UCI Machine Learning Repository. The calculated accuracies by our classifier are 66.9645%, 59.7696%, and 45.8487% for k-NN, Descion Tree and Naïve Bayes’ Classifier respectively. k-NN gives the maximum accuracy while the previously calculated accuracy of k-NN was 53%.

Index Terms—accuracy, arrhythmia, descion tree classifier, k-NN classifier, naïve bayes classifier

Cite: Saleha Samad, Shoab A. Khan, Anam Haq, and Amna Riaz, "Classification of Arrhythmia," International Journal of Electrical Energy, Vol. 2, No. 1, pp. 57-61, March 2014. doi: 10.12720/ijoee.2.1.57-61
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