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Dengue remains a globally prevalent and potentially fatal disease, affecting millions of people worldwide each year. Early and accurate detection of dengue complications is crucial to improving clinical outcomes and reducing the burden on healthcare systems. In this study, we explore the use of computational simulations based on fuzzy cognitive maps (FCMs) to improve the detection of dengue complications. We propose an innovative approach that integrates clinical data into a computational model that mimics the decision-making process of a medical expert. Our method uses FCMs to model complexity and uncertainty in dengue. The model was evaluated in simulated scenarios with each of the dengue classifications. These maps allow us to represent and process vague and fuzzy information effectively, capturing relationships that often go unnoticed in conventional approaches. The results of the simulations show the potential of our approach to detecting dengue complications. This innovative strategy has the potential to transform the way clinical management of dengue is approached. This research is a starting point for further development of complication detection approaches for events of public health concern, such as dengue. [ABSTRACT FROM AUTHOR]

Dengue is a viral infection widely distributed in tropical and subtropical regions of the world. Dengue is characterized by high fatality rates when the diagnosis is not made promptly and effectively. To aid in the diagnosis of dengue, we propose a clinical decision-support system that classifies the clinical picture based on its severity, and using causal relationships evaluates the behavior of the clinical and laboratory variables that describe the signs and symptoms related to dengue. The system is based on a fuzzy cognitive map that is defined by the signs, symptoms and laboratory tests used in the conventional diagnosis of dengue. The evaluation of the model was performed on datasets of patients diagnosed with dengue to compare the model with other approaches. The developed model showed a good classification performance with 89.4% accuracy and could evaluate the behaviour of clinical and laboratory variables related to dengue severity (it is an explainable method). This model serves as a diagnostic aid for dengue that can be used by medical professionals in clinical settings. [ABSTRACT FROM AUTHOR]