This study presents a novel propositional fuzzy predicate cardiovascular risk ponderation. Arterial Diameter Variation signal assessment was performed based on compensatory fuzzy logic. The proposed predicates integrate signal processing techniques, expert knowledge, and fuzzy predicates mechanisms to support the estimation of cardiovascular risk. Arterial signals, which reflect changes in arterial diameter due to hemodynamic alterations, were acquired using non-invasive tools and subjected to a preprocessing stage that included feature extraction calculations. Subsequently, a fuzzy predicate was implemented, incorporating clinical expertise in cardiovascular risk determination. Compensatory logic evaluates Arterial dynamics in order to determine cardiovascular risk. The result was validated using real patient data and expert evaluations, showing a high correlation between the model’s outputs and specialist assessments. This approach offers a robust and interpretable tool for supporting cardiovascular diagnosis and monitoring, particularly in scenarios with limited access to specialized care.