Navigating Cardiac Health through Machine Learning – A Literature Survey
Dr. Sharmila Rathod, Simra Bhombal, Siddhesh S. Ghadi, Sakshi jangir
Machine Learning, Cardiovascular Disease, Electrocardiogram (ECG), Electronic Health Record, Predictive Model, Cardiology, Risk Assessment, Medical Imaging
In this comprehensive literature survey, the current state of knowledge regarding Cardiac disease and Machine Learning algorithms used for this purpose is explored. Our investigation encompasses a wide range of scholarly sources, including peer-reviewed articles and reports, to provide a comprehensive overview of the existing research landscape. Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, necessitating advanced tools and techniques for early detection, risk assessment, and personalized treatment. Throughout this exploration, we investigate various facets of ML in Cardiology, including the development of predictive models, risk assessment techniques, and the application of ML in medical imaging and Electronic Health Records (EHR) analysis. This study focuses on the application of machine learning algorithms in the analysis of Electrocardiogram (ECG) data for cardiac disease detection. Machine learning (ML) has emerged as a promising avenue to augment traditional medical practices, offering innovative solutions for navigating cardiac health. This review will also encourage researchers, scholars and curious minds to get guidance for future research and innovation.
Article Details
Unique Paper ID: 161472

Publication Volume & Issue: Volume 10, Issue 4

Page(s): 298 - 301
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