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dc.contributor.authorUnnikrishnan, P
dc.contributor.authorKumar, DK
dc.contributor.authorArjunan, SP
dc.contributor.authorKumar, H
dc.contributor.authorMitchell, P
dc.contributor.authorKawasaki, R
dc.date.accessioned2021-02-05T00:56:23Z
dc.date.available2021-02-05T00:56:23Z
dc.date.issued2016-01-01
dc.identifier.citationUnnikrishnan, 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.
dc.identifier.issn1748-670X
dc.identifier.urihttp://hdl.handle.net/11343/260213
dc.description.abstractCurrent 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.
dc.languageEnglish
dc.publisherHINDAWI LTD
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleDevelopment of Health Parameter Model for Risk Prediction of CVD Using SVM
dc.typeJournal Article
dc.identifier.doi10.1155/2016/3016245
melbourne.affiliation.departmentOphthalmology (Eye & Ear Hospital)
melbourne.affiliation.facultyMedicine, Dentistry & Health Sciences
melbourne.source.titleComputational and Mathematical Methods in Medicine
melbourne.source.volume2016
dc.rights.licenseCC BY
melbourne.elementsid1098092
melbourne.contributor.authorKawasaki, Ryo
dc.identifier.eissn1748-6718
melbourne.accessrightsOpen Access


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