Long term cardiovascular risk models' combination - a new approach.
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.