Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/4A624E5B-EFA7-412D-BC93-F7572E1D3171 | - |
| Αναγνωριστικό | https://doi.org/10.1109/EMBC46164.2021.9630119 | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/9630119 | - |
| Γλώσσα | en | - |
| Μέγεθος | 4 pages | en |
| Τίτλος | Machine learning model for predicting CVD risk on NHANES data | en |
| Δημιουργός | Klados Georgios | en |
| Δημιουργός | Κλαδος Γεωργιος | el |
| Δημιουργός | Politof Konstantinos | en |
| Δημιουργός | Πολιτωφ Κωνσταντινος | el |
| Δημιουργός | Bei Aikaterini | en |
| Δημιουργός | Μπεη Αικατερινη | el |
| Δημιουργός | Moirogiorgou Konstantia | en |
| Δημιουργός | Μοιρογιωργου Κωνσταντια | el |
| Δημιουργός | Anousakis-Vlachochristou N. | en |
| Δημιουργός | Matsopoulos, George K | en |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| 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. | en |
| Τύπος | Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Publication | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2023-05-16 | - |
| Ημερομηνία Δημοσίευσης | 2021 | - |
| Θεματική Κατηγορία | Heart | en |
| Θεματική Κατηγορία | Economics | en |
| Θεματική Κατηγορία | Machine learning | en |
| Θεματική Κατηγορία | Predictive models | en |
| Θεματική Κατηγορία | Tools | en |
| Θεματική Κατηγορία | Feature extraction | en |
| Θεματική Κατηγορία | Data models | en |
| 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. | en |
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