Detection of Cardiovascular Diseases Using Machine Learning and Deep Learning

EasyChair Preprint 9880

Abstract

Cardiovascular diseases also known as heart diseases are the leading cause of death on a global scale. Early detection of cardiac abnormalities can save many lives and even assist physicians in developing an effective treatment plan. An electrocardiogram (ECG), a common and inexpensive way of detecting the electrical activity of the heart, can be used to diagnose cardiovascular diseases. Numerous studies have been conducted on the use of machine learning algorithms to detect heart disease, though the majority of these models do not provide especially high accuracy. This project used the publicly available ECG image dataset of cardiac patients to identify four main cardiac abnormalities: myocardial infarction, history of myocardial infarction, abnormal heartbeat, and normal person classes.

Keyphrases: Cardiovascular diseases, ECG images, and Machine Learning, deep learning, feature extraction

@booklet{EasyChair:9880,
  author    = {Pratipal Rai and Shanelle Kalonice Fernandes and Tenzin Choedon and Tseten Tashi Bhutia},
  title     = {Detection of Cardiovascular Diseases Using Machine Learning and Deep Learning},
  howpublished = {EasyChair Preprint 9880},
  year      = {EasyChair, 2023}}