Cardiovascular risk prediction based on retinal vessel analysis using machine learning

Karma Fathalla, Anikó Ekárt, Swathi Seshadri, Doina Gherghel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cardiovascular risk prediction is a vital aspect of personalized health care. In this study, retinal vascular function is assessed in asymptomatic participants who are classified into risk groups based on Framingham Risk Score. Feature selection, oversampling and state-of-the-art classification methods are applied to provide a sound individual risk prediction based on Retinal Vessel Analysis (RVA) data obtained by non-invasive methods. The results indicate that the RVA based cardiovascular risk prediction models are competitive with well established Framingham and Qrisk based models.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PublisherIEEE
Pages880-885
Number of pages6
ISBN (Electronic)978-1-5090-1-897-0
DOIs
Publication statusPublished - 6 Feb 2017
Event2016 IEEE International Conference on Systems, Man and Cybernetics - Budapest, Hungary
Duration: 9 Oct 201612 Oct 2016
http://www.smc2016.org/

Conference

Conference2016 IEEE International Conference on Systems, Man and Cybernetics
Abbreviated titleSCM 2016
CountryHungary
CityBudapest
Period9/10/1612/10/16
Internet address

Bibliographical note

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Keywords

  • cardiovascular risk
  • machine learning
  • prediction

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  • Cite this

    Fathalla, K., Ekárt, A., Seshadri, S., & Gherghel, D. (2017). Cardiovascular risk prediction based on retinal vessel analysis using machine learning. In 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings (pp. 880-885). IEEE. https://doi.org/10.1109/SMC.2016.7844352