Analysis and implementation of clustering in dengue cases using unsupervised learning algorithm
DOI:
https://doi.org/10.24054/rcta.v2i44.3021Keywords:
Unsupervised Learning, Clustering, Dengue, SegmentationAbstract
This study focuses on the application of unsupervised learning algorithms, specifically clustering techniques, to analyze the incidence of dengue in San Juan, Puerto Rico, and Iquitos, Peru. The main objective is to evaluate the effectiveness of these algorithms in identifying hidden patterns in the data set, composed of environmental, climatic information and dengue cases. The research allowed us to verify the importance of selecting the appropriate clustering technique, evidenced by the variable performance of the methods used. The results reveal the usefulness of unsupervised learning for understanding the spread of dengue, highlighting the need to carefully consider the choice of algorithm for future epidemiological and environmental analyses.
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