Machine learning model for predicting CVD risk on NHANES dataMachine learning model for predicting CVD risk on NHANES data Δημοσίευση σε Συνέδριο Conference Publication 2023-05-162021enResearch has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: Τ1ΕΔΚ-03505, e-MASS)Cardiovascular 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.http://creativecommons.org/licenses/by/4.0/1749-17522021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society Klados Georgios Κλαδος Γεωργιος Politof Konstantinos Πολιτωφ Κωνσταντινος Bei Aikaterini Μπεη Αικατερινη Moirogiorgou Konstantia Μοιρογιωργου Κωνσταντια Anousakis-Vlachochristou N. Matsopoulos, George K Zervakis Michail Ζερβακης Μιχαηλ Institute of Electrical and Electronics Engineers Heart Economics Machine learning Predictive models Tools Feature extraction Data models