1. Methodology for the Differential Classification of Dengue and Chikungunya According to the PAHO 2022 Diagnostic Guide.
- Author
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Arrubla-Hoyos, Wilson, Gómez, Jorge Gómez, and De-La-Hoz-Franco, Emiro
- Subjects
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CHIKUNGUNYA , *MACHINE learning , *ARBOVIRUSES , *SYMPTOMS , *INTERPOLATION - Abstract
Arboviruses such as dengue, Zika, and chikungunya present similar symptoms in the early stages, which complicates their differential and timely diagnosis. In 2022, the PAHO published a guide to address this challenge. This study proposes a methodological framework that transforms qualitative information into quantitative information, establishing differential weights in relation to symptoms according to the medical evidence and the GRADE scale based on recommendation 1 of the said guide. To achieve this, common variables from the dataset were identified using the PAHO guide, and quality rules were established. A linear interpolation function was then parameterised to assign weights to the symptoms according to the evidence. Machine learning was used to compare the different models, achieving 99% accuracy compared with 79% without the methodology. This proposal represents a significant advancement, allowing the direct application of the PAHO recommendations to the dataset and improving the differential classification of arboviruses. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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