Manuscript Number : CSEIT228211

Machine Learning Algorithms for Predicting Heart Disease

Authors(2) :-Trinath Katari, Dr. B. Madhava Rao

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.

Authors and Affiliations

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

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

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Publication Details

Published in : Volume 8 | Issue 1 | January-February 2022
Date of Publication : 2022-02-15
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 301-306
Manuscript Number : CSEIT228211
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Trinath Katari, Dr. B. Madhava Rao, "Machine Learning Algorithms for Predicting Heart Disease", International 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. |          | BibTeX | RIS | CSV

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