Design of Random Forest Algorithm Based Model for Tachycardia Detection

ECG signals are need to be analyzed accurately for better diagnosis. Different parameters of ECG signals provide information regarding the heart disease. In this paper, an attempt has been made to detect tachycardia, a class of arrhythmia. With the help of random forest algorithm, the updated technique has been utilized for the cardiac signal classification and detection. Thirteen attributes are considered as the input to the model. The technique is multiple decision trees based with each tree size is considered as 150. As compared to earlier methods the proposed method found better classification accuracy.

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