A comparative study of machine learning approaches for proactive cardiovascular disease prediction

Authors

  • B. Kalaivani Research Scholar, Department of Computer Science, Sri S.Ramasamy Nadiu Memorial College, Sattur, India
  • A. Ranichitra Assistant Professor, Department of Computer Science, Sri S.Ramasamy Nadiu Memorial College, Sattur, India

Keywords:

cardiovascular disease (CVD), machine learning (ML), deep learning (DL), feature extraction

Abstract

The most common cause of death worldwide is cardiovascular disease (CVD), and its prediction is both difficult and crucial in the medical field. Bad eating habits, lack of exercise, cigarette and alcohol use, high blood pressure, elevated fasting, increased blood lipid levels, and being overweight or obese are the main behavioral causes of disease and stroke. Machine learning has emerged as an important tool in making healthcare decisions and predictions from healthcare information. This paper's goal is to evaluate and examine the risk factors for heart disease prediction as well as the different Machine Learning methods put out by different researchers for heart disease classification and prediction. This paper highlights and compares the algorithms and Methodologies used for early prediction of heart disease in order to discover the best technique to control the mortality rate in case of earlier detection of heart disease and adopting some preventive measures.

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Published

18-10-2022

How to Cite

Kalaivani, B., & Ranichitra, A. (2022). A comparative study of machine learning approaches for proactive cardiovascular disease prediction. International Journal of Health Sciences, 6(S8), 5390–5400. Retrieved from https://sciencescholar.us/journal/index.php/ijhs/article/view/13462

Issue

Vol. 6 No. S8 (2022)

Section

Peer Review Articles
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