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    Development of Health Parameter Model for Risk Prediction of CVD Using SVM

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    15
    Author
    Unnikrishnan, P; Kumar, DK; Arjunan, SP; Kumar, H; Mitchell, P; Kawasaki, R
    Date
    2016-01-01
    Source Title
    Computational and Mathematical Methods in Medicine
    Publisher
    HINDAWI LTD
    University of Melbourne Author/s
    Kawasaki, Ryo
    Affiliation
    Ophthalmology (Eye & Ear Hospital)
    Metadata
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    Document Type
    Journal Article
    Citations
    Unnikrishnan, P., Kumar, D. K., Arjunan, S. P., Kumar, H., Mitchell, P. & Kawasaki, R. (2016). Development of Health Parameter Model for Risk Prediction of CVD Using SVM. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE, 2016, https://doi.org/10.1155/2016/3016245.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/260213
    DOI
    10.1155/2016/3016245
    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.

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