A Bayesian network model for predicting cardiovascular risk

TL;DR

This paper introduces a Bayesian network model that predicts cardiovascular risk using a large Spanish dataset, incorporating uncertainty and supporting public health decision-making.

Contribution

The paper presents a novel Bayesian network model with data-driven structure and probability tables, including uncertainty quantification, for cardiovascular risk prediction.

Findings

Model effectively predicts cardiovascular risk.

Uncertainty in predictions is quantified.

Software implementation is freely available.

Abstract

We propose a Bayesian network model to make inferences and predictions about cardiovascular risk. Both the structure and the probability tables in the underlying model are built using a large dataset collected in Spain from annual work health assessments, with uncertainty characterized through posterior distributions. We illustrate its use for public health practice, policy and research purposes. A freely available version of the software is included in an Appendix.