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. 2024 Mar 2;14(5):533.
doi: 10.3390/diagnostics14050533.

Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications

William Hoyos  1   2 , Kenia Hoyos  3 , Rander Ruíz  4
Affiliations

Using Computational Simulations Based on Fuzzy Cognitive Maps to Detect Dengue Complications

William Hoyos et al. Diagnostics (Basel). .

Abstract

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.

Keywords: artificial intelligence; complications; computer-aided systems; dengue; fuzzy cognitive maps.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Example of an FCM consisting of 9 nodes representing concepts, and arrows indicating their relationships. The concepts C1 through C8 (white color) are the predictor variables and C9 (blue color) is the target concept.
Figure 2
Figure 2
Flowchart of the methodology used in this research.
Figure 3
Figure 3
Global FCM model as a computer-aided system to predict dengue complications.
Figure 4
Figure 4
Evolution of attributes in a dengue patient without warning signs. Dashed gray line indicates when the simulation reached equilibrium.
Figure 5
Figure 5
Evolution of attributes in a patient who has dengue with warning signs. Dashed gray line indicates when the simulation reached equilibrium.
Figure 6
Figure 6
Evolution of attributes in a patient with severe dengue. Dashed gray line indicates when the simulation reached equilibrium.
See this image and copyright information in PMC

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