Development of Health Parameter Model for Risk Prediction of CVD Using SVM. (9th August 2016)

• Record Type: Journal Article
• Authors: Unnikrishnan, P.; Kumar, D. K.; Poosapadi Arjunan, S.; Kumar, H.; Mitchell, P.; Kawasaki, R.
• Other Names: Arbeev Konstantin G. Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2016(2016)
• Publication Date: 2016-08-09
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2016/3016245
• Languages: English
• ISSNs: 1748-670X

Abstract

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.