Cardiac Arrhythmia Detection Using Machine Learning

Abstract
One of the most important organs for maintaining human life is the heart. Its normal functioning is crucial, but its irregular functioning can lead to a variety of issues that could be categorized as distinct heart diseases. Arrhythmia is an irregular heartbeat that falls under the category of cardiovascular and vascular diseases. The electrocardiogram, or ECG, is the method of choice for recording heartbeats. This could result in sudden death, blood clots, heart failure, stroke, etc. if proper precautions are not taken. Also, cardiac arrhythmia, often commonly known as a serious type of heart arrhythmia, encompasses a variety of arrhythmias that affect the heart and has been the leading cause of mortality globally in recent decades. It is linked to numerous risks in heart arrhythmia and a current need for accurate, trustworthy, and reasonable methods to establish an early diagnosis in order to achieve early arrhythmia treatment. In the healthcare sector, data analysis is a widely utilized method for processing massive amounts of data. Algorithms are the subject of the study of machine learning. Researchers use a variety of statistical and machine learning methods to evaluate massive amounts of complicated medical data, assisting healthcare practitioners in predicting cardiac arrhythmia. This study covers many aspects of cardiac arrhythmia as well as a model based on supervised learning techniques such as random forest, decision tree, and logistic regression. The purpose of this study is to forecast the likelihood of individuals getting heart arrhythmia. The findings show that logistic regression achieves the best accuracy score (80.10 percent)
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Electrical and Computer Engineering
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North South University
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