Comparação de modelos de Machine Learning aplicados a previsão de casos totais de Dengue

• Thiago Carvalho Pontifícia Universidade Católica do Rio de Janeiro
• Gabriel Tenório Pontifícia Universidade Católica do Rio de Janeiro
• Karla Figueiredo Universidade do Estado do Rio de Janeiro
• Marley Vellasco Pontifícia Universidade Católica do Rio de Janeiro
• Wouter Caarls Pontifícia Universidade Católica do Rio de Janeiro

DOI: https://doi.org/10.5753/eniac.2019.9323

Resumo

A dengue e uma doença endêmica que ocorre principalmente em áreas tropicais, devido à sua transmissão através de mosquitos. Usando mecanismos de pré-processamento e de aprendizado de máquina, esse trabalho objetiva desenvolver um modelo de previsão que estabeleça uma relação existente entre as condicões de uma cidade e a proliferação de epidemia de dengue, como parte da competição 'DengAI - predicting disease spread', fornecida pela plataforma DrivenData. Dentre os modelos implementados, o metodo Ensemble entre o Random Forest e Redes Neurais obtiveram a melhor performance, com melhora de 4,5% em relação ao Benchmark.

Palavras-chave: Data Mining, Aprendizado de Máquina, Dengue, Regressão, Redes Neurais Artificiais

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