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A Bayesian network model for predicting cardiovascular risk
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DOI：10.1016/j.cmpb.2023.107405.png)
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Abstract
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Background and Objective: Cardiovascular diseases are the leading death cause in Europe and entail large treatment costs. Cardiovascular risk prediction is crucial for the management and control of cardiovascu-lar diseases. Based on a Bayesian network built from a large population database and expert judgment, this work studies interrelations between cardiovascular risk factors, emphasizing the predictive assess-ment of medical conditions, and providing a computational tool to explore and hypothesize such interre-lations.Methods: We implement a Bayesian network model that considers modifiable and non-modifiable cardio-vascular risk factors as well as related medical conditions. Both the structure and the probability tables in the underlying model are built using a large dataset collected from annual work health assessments as well as expert information, with uncertainty characterized through posterior distributions.Results: The implemented model allows for making inferences and predictions about cardiovascular risk factors. The model can be utilized as a decision-support tool to suggest diagnosis, treatment, policy, and research hypothesis. The work is complemented with a free software implementing the model for practitioners' use. Conclusions: Our implementation of the Bayesian network model facilitates answering public health, pol-icy, diagnosis, and research questions concerning cardiovascular risk factors.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Keywords:
Bayesian network
Cardiovascular diseases
Healthcare
Disease treatment
Health policy
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