Identifying Biomarkers of Cardiovascular Diseases with Machine Learning: Evidence from The UK Household Longitudinal Study

Main Article Content

Vasilis Nikolaou Sebastiano Massaro Masoud Fakhimi Wolfgang Garn

Abstract

Cardiovascular diseases  are a significant global health concern, responsible for one-third of deaths worldwide and posing a substantial burden on society and national healthcare systems. To effectively address this challenge and develop targeted intervention strategies, the ability to predict cardiovascular diseases from standardized assessments, such as occupational health encounters or national surveys, is critical. This study aims to assist these efforts by identifying a set of biomarkers, which together with known risk factors, can predict cardiovascular diseases on the onset. We used a sample of 7,767 individuals from the UK household longitudinal study ‘Understanding Society’ to train several machine learning models able to pinpoint biomarkers and risk factors at baseline that predict cardiovascular diseases at a ten-year follow-up. A logistic regression model was trained for comparison. A gaussian naïve bayes classifier returned 82% recall in contrast to 48% of the logistic regression, allowing us to identify the most prominent biomarkers predicting cardiovascular diseases. These findings show the opportunity to use machine learning to identify a wide range of previously overlooked biomarkers associated with cardiovascular diseases onset and thus encourage the implementation of such a model in the early diagnosis and prevention of cardiovascular diseases in future research and practice.

Keywords: Machine Learning, Naïve Bayes, Logistic Regression, Biomarkers, Cardiovascular diseases

Article Details

How to Cite
NIKOLAOU, Vasilis et al. Identifying Biomarkers of Cardiovascular Diseases with Machine Learning: Evidence from The UK Household Longitudinal Study. Medical Research Archives, [S.l.], v. 12, n. 1, jan. 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/4966>. Date accessed: 03 mar. 2024. doi: https://doi.org/10.18103/mra.v12i1.4966.
Section
Research Articles

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