Contribuciones del aprendizaje automático en el descubrimiento del dengue: un análisis cienciométrico

Wilson Arrubla-Hoyos, Andrés Solano-Barliza

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Resumen

El dengue es una enfermedad vírica que cobra vidas humanas año tras año, lo que genera la necesidad de explorar nuevas soluciones desde la informática para lograr una detección temprana y eficaz. Este estudio tuvo como objetivo identificar las tendencias de investigación que vinculan las técnicas de aprendizaje automático (machine learning) con el dengue. Para este fin, se realizó un análisis cienciométrico y sistemático, que comenzó con una búsqueda de aprendizaje automático y dengue en Scopus sin restricciones temporales. Se hallaron 377 documentos publicados entre 2010 y 2022. Posteriormente, se aplicó la técnica PRISMA y se filtraron los documentos a partir de los criterios de inclusión y exclusión para asegurar la calidad del análisis. Mediante el empleo de herramientas como R Studio, la biblioteca biblioshiny de bibliometrix y VOSviewer se examinaron los elementos clave de la producción científica como: países, autores destacados, revistas relevantes y co-ocurrencias de palabras clave. Los resultados permitieron identificar tres áreas de enfoque: diagnóstico del dengue, pronóstico del dengue y control de mosquitos. Se encontró que la investigación en el uso del aprendizaje automático para detectar el dengue ha crecido de manera constante y ha atraído a más investigadores a partir de 2016. Las técnicas de aprendizaje automático más utilizadas son: Artificial Neural Network (ANN), Decision Tree, Support Vector Machine (SVM) y una tendencia a usar Deep learning. Por su parte, el área del diagnóstico utiliza variables meteorológicas como humedad, temperatura y lluvias para realizar los pronósticos de los brotes del dengue.

Palabras clave

machine learning; dengue; diagnóstico; pronóstico; variables meteorológicas.

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