An Experiment in Feature Engineering for Generating Predictive Models for Dengue Cases

• Ramon Garcia Federal Center for Technological Education Celso Suckow da Fonseca (CEFET/RJ)
• Eduardo Ogasawara Federal Center for Technological Education Celso Suckow da Fonseca (CEFET/RJ)
• Jorge Soares Federal Center for Technological Education Celso Suckow da Fonseca (CEFET/RJ)
• Amaury de Souza Federal University of Mato Grosso do Sul (UFMS)
• Rejane Sobrino Federal University of Rio de Janeiro (UFRJ)
• Eduardo Bezerra Federal Center for Technological Education Celso Suckow da Fonseca (CEFET/RJ)

DOI: https://doi.org/10.5753/bresci.2024.243949

Abstract

This study aims to develop machine learning models to predict the number of dengue cases in a given health facility. Our approach involves feature engineering by integrating data from various domains. Specifically, we combine data from Brazil’s Unified Health System with meteorological data from the National Institute of Meteorology and the GOES-16 weather satellite. We train Long Short-Term Memory neural networks to generate predictive models that capture climatic patterns and their influences on dengue incidence, considering both spatial and temporal data.

Keywords: Models, Dengue, prediction, feature engineering

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