Effective Arrhythmia Detection using Majority Voting

Abstract

Heart disease is the second leading cause of death in Singapore as reported by the Ministry of Health, Singapore. Research shows that stress and mental anxiety are the main causes of heart diseases. The risk of stroke is five times greater in people with atrial fibrillation, which makes the latter one of the leading cause of death in Singapore. This paper deals with classification of the patients into various conditions of arrhythmia. Each time a patient visits a hospital, the patient may get different opinions from different doctors about the same problem. There is no data-driven or evidential decision-making process in the sphere of health. Hence, a novel approach is proposed to help the doctors arrive at a proper conclusion about the patient's condition using various machine learning algorithms and ensemble techniques for classifying the patient condition.