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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

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/4A624E5B-EFA7-412D-BC93-F7572E1D3171
Έτος 2021
Τύπος Δημοσίευση σε Συνέδριο
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά G. 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. https://doi.org/10.1109/EMBC46164.2021.9630119
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Περίληψη

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.

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