A clinical decision-support system for dengue based on fuzzy cognitive maps

William Hoyos (), Jose Aguilar () and Mauricio Toro ()
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William Hoyos: Universidad de Córdoba
Jose Aguilar: Universidad EAFIT
Mauricio Toro: Universidad EAFIT

Health Care Management Science, 2022, vol. 25, issue 4, No 8, 666-681

Abstract: Abstract 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.

Keywords: Machine learning; Dengue; Artificial intelligence; Diagnosis; Fuzzy cognitive maps; Clinical decision-support system (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10729-022-09611-6

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