Open Access

ARTICLE

Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

Visvasam Devadoss Ambeth Kumar¹, Chetan Swarup², Indhumathi Murugan¹, Abhishek Kumar³, Kamred Udham Singh⁴, Teekam Singh⁵, Ramu Dubey^6,*
1 Department of Computer Science & Engineering, Panimalar Engineering College, Anna University, Chennai, 600123, India
2 Department of Basic Science, College of Science & Theoretical Studies, Saudi Electronic University, Riyadh-Male Campus 13316, Saudi Arabia
3 Department of Computer Science & IT, JAIN (Deemed to be University), Bangalore, 560069, India
4 Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
5 Department of Mathematics, Graphic Era Hill University, Dehradun, 248002, India
6 Department of Mathematics, J. C. Bose University of Science & Technology, YMCA, Faridabad, 121006, India
* Corresponding Author: Ramu Dubey. Email:
(This article belongs to this Special Issue: Applications of Machine Learning for Big Data)

Computers, Materials & Continua 2022, 71(1), 855-869. https://doi.org/10.32604/cmc.2022.021582

Received 07 July 2021; Accepted 23 August 2021; Issue published 03 November 2021

Abstract

Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main aim of this research is to predict the premature heart disease based on machine learning algorithms. This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself. Support Vector Machine (SVM) is used for segregating the CVD patients based on their symptoms and medical observation. The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time. A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.

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Keywords

Machine learning; support vector machine; classification; cardiovascular disease

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