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Machine learning model for predicting CVD risk on NHANES data

Klados Georgios, Politof Konstantinos, Bei Aikaterini, Moirogiorgou Konstantia, Anousakis-Vlachochristou N., Matsopoulos, George K, Zervakis Michail

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URIhttp://purl.tuc.gr/dl/dias/4A624E5B-EFA7-412D-BC93-F7572E1D3171-
Identifierhttps://doi.org/10.1109/EMBC46164.2021.9630119-
Identifierhttps://ieeexplore.ieee.org/document/9630119-
Languageen-
Extent4 pagesen
TitleMachine learning model for predicting CVD risk on NHANES dataen
CreatorKlados Georgiosen
CreatorΚλαδος Γεωργιοςel
CreatorPolitof Konstantinosen
CreatorΠολιτωφ Κωνσταντινοςel
CreatorBei Aikaterinien
CreatorΜπεη Αικατερινηel
CreatorMoirogiorgou Konstantiaen
CreatorΜοιρογιωργου Κωνσταντιαel
CreatorAnousakis-Vlachochristou N.en
CreatorMatsopoulos, George Ken
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryCardiovascular disease (CVD) is a major health problem throughout the world. It is the leading cause of morbidity and mortality and also causes considerable economic burden to society. The early symptoms related to previous observations and abnormal events, which can be subjectively acquired by self-assessment of individuals, bear significant clinical relevance and are regularly preserved in the patient’s health record. The aim of our study is to develop a machine learning model based on selected CVD-related information encompassed in NHANES data in order to assess CVD risk. This model can be used as a screening tool, as well as a retrospective reference in association with current clinical data in order to improve CVD assessment. In this form it is planned to be used for mass screening and evaluation of young adults entering their army service. The experimental results are promising in that the proposed model can effectively complement and support the CVD prediction for the timely alertness and control of cardiovascular problems aiming to prevent the occurrence of serious cardiac events.en
Type of ItemΔημοσίευση σε Συνέδριοel
Type of ItemConference Publicationen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-05-16-
Date of Publication2021-
SubjectHearten
SubjectEconomicsen
SubjectMachine learningen
SubjectPredictive modelsen
SubjectToolsen
SubjectFeature extractionen
SubjectData modelsen
Bibliographic CitationG. A. Klados, K. Politof, E. S. Bei, K. Moirogiorgou, N. Anousakis-Vlachochristou, G. K. Matsopoulos and M. Zervakis, "Machine learning model for predicting CVD risk on NHANES data," in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, 2021, pp. 1749-1752, doi: 10.1109/EMBC46164.2021.9630119.en

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