Análisis e implementación de clustering en casos de dengue mediante algoritmo de aprendizaje no supervisado

Miguel Alberto Rincón Pinzón; Carlos Alberto Mejía Rodríguez; Erney Alberto Ramírez Camargo; Lina Marcela Arévalo Vergel

doi:10.24054/rcta.v2i44.3021

Autores/as

Miguel Alberto Rincón Pinzón Universidad Popular del Cesar Seccional Aguachica https://orcid.org/0000-0002-6827-5209
Carlos Alberto Mejía Rodríguez Universidad Popular del Cesar Seccional Aguachica https://orcid.org/0000-0001-5084-6010
Erney Alberto Ramírez Camargo Universidad Popular del Cesar Seccional Aguachica https://orcid.org/0000-0003-2849-3890
Lina Marcela Arévalo Vergel Universidad Popular del Cesar Seccional Aguachica https://orcid.org/0000-0002-7731-5444

DOI:

https://doi.org/10.24054/rcta.v2i44.3021

Palabras clave:

Aprendizaje No Supervisado, Clustering, Dengue, Segmentación

Resumen

Este estudio se enfoca en la aplicación de algoritmos de aprendizaje no supervisado, específicamente técnicas de clustering para analizar la incidencia del dengue en San Juan, Puerto Rico, e Iquitos, Perú. El objetivo principal es probar la eficacia de estos algoritmos en la identificación de patrones ocultos en el conjunto de datos, compuesto por información ambiental, climática y casos de dengue. La investigación permitió comprobar la importancia de seleccionar la técnica de clusterización adecuada, evidenciada por el rendimiento variable de los métodos utilizados. Los resultados revelan la utilidad del aprendizaje no supervisado para comprender la propagación del dengue, resaltando la necesidad de considerar cuidadosamente la elección del algoritmo para análisis epidemiológicos y ambientales futuros.

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