Long Term Cardiovascular Risk Models' Combination - A new aproach

Authors

Abstract

This work addresses two major drawbacks of the
current cardiovascular risk score systems: reduced number of
risk factors considered by each individual tool and the inability
of these tools to deal with incomplete information. To achieve
this goal a two phase strategy was followed. In the first phase, a
common representation procedure was considered, based on a
Naïve-Bayes classifier methodology. Conditional probabilities
parameters were initially evaluated through a frequency
estimation method and after that optimized using a Genetic
Algorithm approach. In a second phase, a combination scheme
was proposed exploiting the particular features of Bayes
probabilistic reasoning.
This strategy was applied to describe and combine SCORE,
ASSIGN and Framingham models. Validation results were
obtained based on individual models, assuming their statistical
correctness. The achieved results are very promising, showing
the potential of the strategy to accomplish the desired goals.

Conference

31th Annual International IEEE EMBS Conference, USA, January 2009

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