Özet:

Abstract The leading reason of death worldwide is due to heart disease and late treatment. The detection and diagnosis of cardiac arrhythmia is tedious and time consuming from arranging expert to analyse a large amount of ECG data. Therefore, detection of cardia arrhythmia by analysis of ECG characteristics using machine learning has become predominant. This paper proposed a web-based system which classify heart disease depending on the patient's ECG values using support vector machine and convolutional neural network. At the end, comparison in between support vector machine and convolutional neural network is also done using evaluation measures like precision, recall and f-measure. This work can be supporting automated tool to cardiologists for the preliminary screening of cardiac arrhythmia patients to know presence or absence of arrhythmia.

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