疾病
医学
警觉
全国健康与营养检查调查
计算机科学
人工智能
环境卫生
人口
病理
药理学
作者
G A Klados,Konstantinos Politof,Ekaterini S. Bei,Konstantia Moirogiorgou,Nikolaos Anousakis-Vlachochristou,Γεώργιος Κ. Ματσόπουλος,Michalis Zervakis
标识
DOI:10.1109/embc46164.2021.9630119
摘要
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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