Abstract

<p>Automatic detection and analysis of arrhythmias from Electrocardiogram (ECG) signal is beginning to take a center stage in <br>recent times, due to delay the nature of ECG signals, coupled with the subjective interpretation of these signals by cardiologist. <br>Arrhythmia detection plays a vital role in diagnosing and managing cardiovascular diseases. With the advancements in machine <br>learning techniques, various supervised algorithms have been employed to improve the accuracy of arrhythmia detection. <br>However, each supervised ML algorithm has its strength and weakness in predicting the various classes of arrhythmia. This <br>research study presents a comparative analysis of five popular supervised machine learning algorithms: support vector machine <br>(SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Naïve Baye (NB) and Decision Tree (DT) when applied to ECG <br>arrhythmia detection with down sampled dataset. The goal is to evaluate and compare the performance of these algorithms in <br>terms of accuracy, precision, recall, and F1 score. The study utilizes the MIT-BIH benchmark dataset, and experimental results <br>provide insights into the strengths and limitations of each algorithm, aiding in the selection of the most suitable algorithm for<br>accurate ECG arrhythmia detection. The Random Forest algorithm outperformed other algorithms in terms of accuracy, <br>achieving an accuracy of 89.9% with RR interval based feature set.</p>