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Reseach Article

Prediction of Cardiovascular Diseases using Support Vector Machine and Bayesian Classification

by Prashasti Kanikar, Disha Rajeshkumar Shah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 2
Year of Publication: 2016
Authors: Prashasti Kanikar, Disha Rajeshkumar Shah
10.5120/ijca2016912368

Prashasti Kanikar, Disha Rajeshkumar Shah . Prediction of Cardiovascular Diseases using Support Vector Machine and Bayesian Classification. International Journal of Computer Applications. 156, 2 ( Dec 2016), 9-13. DOI=10.5120/ijca2016912368

@article{ 10.5120/ijca2016912368,
author = { Prashasti Kanikar, Disha Rajeshkumar Shah },
title = { Prediction of Cardiovascular Diseases using Support Vector Machine and Bayesian Classification },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 2 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume156/number2/26679-2016912368/ },
doi = { 10.5120/ijca2016912368 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:01:29.336599+05:30
%A Prashasti Kanikar
%A Disha Rajeshkumar Shah
%T Prediction of Cardiovascular Diseases using Support Vector Machine and Bayesian Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 2
%P 9-13
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cardiovascular disease is a broad team for a range of diseases affecting heart and blood vessels. Cardiovascular disease are the number one cause of death globally. The health care industry contains lots of medical data, therefore data mining techniques are required to discover hidden patterns and to make decision effectively in prediction of heart diseases. By applying data mining techniques, valuable knowledge can be extracted from health care systems. Data mining classification techniques like Naïve Bayesian and Support vector machine (SVM) are explained in this paper with their benefits and limitations. Data mining will help doctors to extract useful information from a huge dataset. In proposed research pre-processing uses techniques like noise removal, discarding records with missing data, filling default values if applicable and classification of attributes for decision making at different levels. This paper has predicted accuracy, specificity and sensitivity using a classifier. A classifier will predict whether a person has heart disease or not by using machine learning techniques like Support Vector Machine (SVM) and Naïve Bayes.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification Support Vector Machine (SVM) Naïve Bayes