Development of Health Parameter Model for Risk Prediction of CVD Using SVM.
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.
Biosignals Lab, School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC 3001, Australia.
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P
Unnikrishnan
Biosignals Lab, School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC 3001, Australia.
D K
0000-0003-3602-4023
DK
Kumar
Biosignals Lab, School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC 3001, Australia.
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0000-0002-7288-0380
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Poosapadi Arjunan
Eastern Health, Melbourne, VIC 3128, Australia.
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H
Kumar
Centre for Vision Research, Department of Ophthalmology, Westmead Millennium Institute, University of Sydney, Sydney, NSW 2006, Australia.
P
P
Mitchell
Department of Public Health, Yamagata University Faculty of Medicine, Yamagata 990-9585, Japan.
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R
Kawasaki
D000368:Aged; D000465:Algorithms; D001315:Australia; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000503:physiopathology*; D015331:Cohort Studies; D016208:Databases, Factual; D005123:Eye / Q000033:anatomy & histology; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D016014:Linear Models; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D010363:Pattern Recognition, Automated; D012372:ROC Curve; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D060388:Support Vector Machine