Cardiovascular Risk Prediction based on Retinal Vessel Analysis using Machine Learning

被引：0

作者：

Fathalla, Karma M. ^{[1
,2
]}

Ekart, Aniko ^{[2
]}

Seshadri, Swathi ^{[3
]}

Gherghe, Doina ^{[3
]}

机构：

[1] Arab Acad Sci & Tehnol, Comp Engn Dept, Alexandria, Egypt

[2] Aston Univ, ALICE, Birmingham, W Midlands, England

[3] Aston Univ, Vasc Res Lab, Birmingham, W Midlands, England

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2016年

关键词：

DIAMETER;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：880 / 885

页数：6

共 50 条

[21] Retinal Blood Vessel Segmentation using an Extreme Learning Machine Approach
Shanmugam, Vasanthi
Banu, R. S. D. Wahida
2013 IEEE POINT-OF-CARE HEALTHCARE TECHNOLOGIES (PHT), 2013, : 318 - 321
[22] Identification of retinal diseases based on retinal blood vessel segmentation using Dagum PDF and feature-based machine learning
Kumar, K. Susheel
Singh, Nagendra Pratap
IMAGING SCIENCE JOURNAL, 2023, 71 (05): : 425 - 445
[23] Machine Learning-Based Prediction of Readmission Risk in Cardiovascular and Cerebrovascular Conditions Using Patient EMR Data
Panchangam, Prasad V. R.
Tejas, A.
Thejas, B. U.
Maniaci, Michael J.
HEALTHCARE, 2024, 12 (15)
[24] Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches
Sajid, Mirza Rizwan
Almehmadi, Bader A.
Sami, Waqas
Alzahrani, Mansour K.
Muhammad, Noryanti
Chesneau, Christophe
Hanif, Asif
Khan, Arshad Ali
Shahbaz, Ahmad
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (23)
[25] Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning
Mettananda, Chamila
Sanjeewa, Isuru
Arachchi, Tinul Benthota
Wijesooriya, Avishka
Chandrasena, Chiranjaya
Weerasinghe, Tolani
Solangaarachchige, Maheeka
Ranasinghe, Achila
Elpitiya, Isuru
Sammandapperuma, Rashmi
Kurukulasooriya, Sujeewani
Ranawaka, Udaya
Pathmeswaran, Arunasalam
Kasturiratne, Anuradhini
Kato, Nei
Wickramasinghe, Rajitha
Haddela, Prasanna
de Silva, Janaka
PLOS ONE, 2024, 19 (10):
[26] Identifying the Main Risk Factors for Cardiovascular Diseases Prediction Using Machine Learning Algorithms
Guarneros-Nolasco, Luis Rolando
Cruz-Ramos, Nancy Aracely
Alor-Hernandez, Giner
Rodriguez-Mazahua, Lisbeth
Sanchez-Cervantes, Jose Luis
MATHEMATICS, 2021, 9 (20)
[27] Cardiovascular disease risk prediction via machine learning using mental health data
Dorraki, M.
Liao, Z.
Abbott, D.
Psaltis, P. J.
Baker, E.
Bidargaddi, N.
Van Den Hengel, A.
Narula, J.
Verjans, J. W.
EUROPEAN HEART JOURNAL, 2022, 43 : 2784 - 2784
[28] Importance and limits of cardiovascular risk factors on the prediction of SCD using machine learning approach
Chocron, R.
Laurenceau, T.
Youssfi, Y.
Bougouin, W.
Empana, J. P.
Chopin, N.
Jouven, X.
EUROPEAN HEART JOURNAL, 2024, 45
[29] Machine Learning based System for Vessel Turnaround Time Prediction
Stepec, Dejan
Martincic, Tomaz
Klein, Fabrice
Vladusic, Daniel
Costa, Joao Pita
2020 21ST IEEE INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (MDM 2020), 2020, : 258 - 263
[30] Prediction of harbour vessel emissions based on machine learning approach
Chen, Zhong Shuo
Lam, Jasmine Siu Lee
Xiao, Zengqi
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2024, 131

← 1 2 3 4 5 →