Cardiovascular risk prediction based on retinal vessel analysis using machine learning

Karma Fathalla; Anikó Ekárt; Swathi Seshadri; Doina Gherghel

doi:10.1109/SMC.2016.7844352

Cardiovascular risk prediction based on retinal vessel analysis using machine learning

Karma Fathalla, Anikó Ekárt, Swathi Seshadri, Doina Gherghel

Research output: Chapter in Book/Published conference output › Conference publication

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 language	English
Title of host publication	2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
Publisher	IEEE
Pages	880-885
Number of pages	6
ISBN (Electronic)	978-1-5090-1-897-0
DOIs	https://doi.org/10.1109/SMC.2016.7844352
Publication status	Published - 6 Feb 2017
Event	2016 IEEE International Conference on Systems, Man and Cybernetics - Budapest, Hungary Duration: 9 Oct 2016 → 12 Oct 2016 http://www.smc2016.org/

Conference

Conference	2016 IEEE International Conference on Systems, Man and Cybernetics
Abbreviated title	SCM 2016
Country/Territory	Hungary
City	Budapest
Period	9/10/16 → 12/10/16
Internet address	http://www.smc2016.org/

Bibliographical note

-

Keywords

cardiovascular risk
machine learning
prediction

Access to Document

10.1109/SMC.2016.7844352

Cite this

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title = "Cardiovascular risk prediction based on retinal vessel analysis using machine learning",

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.",

keywords = "cardiovascular risk, machine learning, prediction",

author = "Karma Fathalla and Anik{\'o} Ek{\'a}rt and Swathi Seshadri and Doina Gherghel",

note = "-; 2016 IEEE International Conference on Systems, Man and Cybernetics, SCM 2016 ; Conference date: 09-10-2016 Through 12-10-2016",

year = "2017",

month = feb,

day = "6",

doi = "10.1109/SMC.2016.7844352",

language = "English",

pages = "880--885",

booktitle = "2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings",

publisher = "IEEE",

address = "United States",

url = "http://www.smc2016.org/",

}

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. IEEE, pp. 880-885, 2016 IEEE International Conference on Systems, Man and Cybernetics, Budapest, Hungary, 9/10/16. https://doi.org/10.1109/SMC.2016.7844352

TY - GEN

T1 - Cardiovascular risk prediction based on retinal vessel analysis using machine learning

AU - Fathalla, Karma

AU - Ekárt, Anikó

AU - Seshadri, Swathi

AU - Gherghel, Doina

N1 - -

PY - 2017/2/6

Y1 - 2017/2/6

N2 - 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.

AB - 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.

KW - cardiovascular risk

KW - machine learning

KW - prediction

UR - http://ieeexplore.ieee.org/document/7844352/

UR - http://www.scopus.com/inward/record.url?scp=85015767803&partnerID=8YFLogxK

U2 - 10.1109/SMC.2016.7844352

DO - 10.1109/SMC.2016.7844352

M3 - Conference publication

AN - SCOPUS:85015767803

SP - 880

EP - 885

BT - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings

PB - IEEE

T2 - 2016 IEEE International Conference on Systems, Man and Cybernetics

Y2 - 9 October 2016 through 12 October 2016

ER -

Cardiovascular risk prediction based on retinal vessel analysis using machine learning

Abstract

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Keywords

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