Development of Health Parameter Model for Risk Prediction of CVD Using SVMReport as inadecuate




Development of Health Parameter Model for Risk Prediction of CVD Using SVM - Download this document for free, or read online. Document in PDF available to download.

Computational and Mathematical Methods in Medicine - Volume 2016 2016, Article ID 3016245, 7 pages -

Research Article

Biosignals Lab, School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC 3001, Australia

Eastern Health, Melbourne, VIC 3128, Australia

Centre for Vision Research, Department of Ophthalmology, Westmead Millennium Institute, University of Sydney, Sydney, NSW 2006, Australia

Department of Public Health, Yamagata University Faculty of Medicine, Yamagata 990-9585, Japan

Received 2 May 2016; Revised 12 July 2016; Accepted 14 July 2016

Academic Editor: Konstantin G. Arbeev

Copyright © 2016 P. Unnikrishnan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.





Author: P. Unnikrishnan, D. K. Kumar, S. Poosapadi Arjunan, H. Kumar, P. Mitchell, and R. Kawasaki

Source: https://www.hindawi.com/



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