A Bayesian network model for predicting cardiovascular risk

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 cardiovascular 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 assessment of medical conditions, and providing a computational tool to explore and hypothesize such interrelations.

We implement a Bayesian network model that considers modifiable and non-modifiable cardiovascular 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.

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.

Our implementation of the Bayesian network model facilitates answering public health, policy, diagnosis, and research questions concerning cardiovascular risk factors.