Machine Learning Algorithms for Predicting Heart Disease

Authors

  • Trinath Katari  M. Tech Student, Sir C R Reddy College of Engineering, Eluru, Andhra Pradesh, India
  • Dr. B. Madhava Rao  Assistant Professor, Department of CSE, Sir C R Reddy College of Engineering, Eluru, Andhra Pradesh, India

Keywords:

Cardiovascular Diseases, Support Vector Machines, K- Nearest Neighbour, Naïve Bayes, Decision Tree, Random Forest

Abstract

Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques and analyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) are found very popular among the researchers.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-02-15

Issue

Volume 8, Issue 1, January-February-2022

Section

Research Articles

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How to Cite

[1]
Trinath Katari, Dr. B. Madhava Rao, " Machine Learning Algorithms for Predicting Heart Disease, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 1, pp.301-306, January-February-2022.