A Clinical Interpretable Approach Applied to Cardiovascular Risk Assessment.

The effectiveness of predictive models in supporting the Clinical Decision is closely related with their clinical interpretability, i.e.the model should provide clear information on how to reach a specific classification/decision. In fact, the development of interpretable and accurate predictive models assumes a key importance as these tools can be very useful in Clinical Decision Support Systems (CDSS). The development of those models may comprise two main perspectives; existent clinical knowledge (clinical expert knowledge, clinical guidelines, current models, etc.) as well as data driven approaches able to extract (new) knowledge from recent clinical datasets. This work focuses in knowledge extraction from recent datasets (data driven) based on computational intelligence techniques. The main hypothesis that supports this work is that individuals with similar characteristics present a similar risk prof ile. Thus, this work addresses the development of stratification models able to learn distinct groups (or classes) of subjects assessing the similarity between characterizing variables. In particular, in the current study a data-driven supervised cluster approach is proposed aiming the derivation of meaningful rules directly from the dataset. The validation was performed based on the largest Portuguese coronary artery disease patient's dataset, provided by the Portuguese Society of Cardiology and comprising 13902 acute coronary syndrome patients. The goal was to assess the risk of death 30 days after admission. The models' performance was assessed through the sensitivity, specificity and geometric mean values. The obtained results show the potential of this approach, as they represent an acceptable performance (GM= 72%) while the clinical interpretability of the model is assured through the derived rules. Despite the achieved results, there are several research directions to be followed in order to enhance this work.