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Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines

by G. Parthiban, A. Rajesh, S. K. Srivatsa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 2
Year of Publication: 2012
Authors: G. Parthiban, A. Rajesh, S. K. Srivatsa
10.5120/7324-0149

G. Parthiban, A. Rajesh, S. K. Srivatsa . Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines. International Journal of Computer Applications. 48, 2 ( June 2012), 45-49. DOI=10.5120/7324-0149

@article{ 10.5120/7324-0149,
author = { G. Parthiban, A. Rajesh, S. K. Srivatsa },
title = { Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 2 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 45-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number2/7324-0149/ },
doi = { 10.5120/7324-0149 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:43:36.104735+05:30
%A G. Parthiban
%A A. Rajesh
%A S. K. Srivatsa
%T Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 2
%P 45-49
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is the analysis step of the Knowledge Discovery in Databases process (KDD). While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process. Data mining techniques are used to operate on large volumes of data to discover hidden patterns and relationships helpful in decision making. Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. Most of these systems have successfully employed Support Vector Machines for the classification purpose. On the evidence of this we too have used SVM classifier using radial basis function kernel for our experimentation. The results of our proposed system were quite good. The system exhibited good accuracy in predicting the vulnerability of diabetic patients to heart diseases.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Diabetes Heart Diseases Knowledge Discovery Support Vector Machines

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