Cardiovascular disease (CVD) causes unaffordable social and health costs that tend to increase as the European population ages. In this context, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Some useful tools have been developed to predict the risk of occurrence of a cardiovascular disease event (e.g. hospitalization or death). However, these tools present some drawbacks. These problems are addressed through two methodologies: (i) combination of risk assessment tools: fusion of naïve Bayes classifiers complemented with a genetic optimization algorithm and (ii) personalization of risk assessment: subtractive clustering applied to a reduced-dimensional space to create groups of patients. Validation was performed based on two ACS-NSTEMI patient data sets. This work improved the performance in relation to current risk assessment tools, achieving maximum values of sensitivity, specificity, and geometric mean of, respectively, 79.8, 83.8, and 80.9 %. Additionally, it assured clinical interpretability, ability to incorporate of new risk factors, higher capability to deal with missing risk factors and avoiding the selection of a standard CVD risk assessment tool to be applied in the clinical practice.
Keywords: Decision-support systems; Information and knowledge management; Management of cardiovascular diseases.