Development of health parameter model for risk prediction of CVD using SVM

Unnikrishnan, P, Kumar, D, Poosapadi Arjunan, S, Kumar, H, Mitchell, P and Kawasaki, R 2016, 'Development of health parameter model for risk prediction of CVD using SVM', Computational and Mathematical Methods in Medicine, vol. 2016, no. 2016, pp. 1-7.


Document type: Journal Article
Collection: Journal Articles

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Title Development of health parameter model for risk prediction of CVD using SVM
Author(s) Unnikrishnan, P
Kumar, D
Poosapadi Arjunan, S
Kumar, H
Mitchell, P
Kawasaki, R
Year 2016
Journal name Computational and Mathematical Methods in Medicine
Volume number 2016
Issue number 2016
Start page 1
End page 7
Total pages 7
Publisher Hindawi Publishing Corporation
Abstract Current methods of cardiovascular risk assessment are performed using health factors which are often based on the Framingham study. However, these methods have significant limitations due to their poor sensitivity and specificity. We have compared the parameters from the Framingham equation with linear regression analysis to establish the effect of training of the model for the local database. Support vector machine was used to determine the effectiveness of machine learning approach with the Framingham health parameters for risk assessment of cardiovascular disease (CVD). The result shows that while linear model trained using local database was an improvement on Framingham model, SVM based risk assessment model had high sensitivity and specificity of prediction of CVD. This indicates that using the health parameters identified using Framingham study, machine learning approach overcomes the low sensitivity and specificity of Framingham model.
Subject Pattern Recognition and Data Mining
Biomedical Engineering not elsewhere classified
DOI - identifier 10.1155/2016/3016245
Copyright notice © 2016 P. Unnikrishnan et al.
ISSN 1748-6718
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