Comparison of Machine Learning Models for Total Dengue Cases Prediction

• 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

Abstract

Dengue is an endemic disease with high prevalence in tropical areas, due to transmission by mosquitoes. Through preprocessing methods and machine learning algorithms, this work aims to develop predictive models for total dengue cases using climatic variables, as part of the ’DengAI-predicting disease spread’ competition, hosted by DrivenData. Among all algorithms implemented, the Ensemble method, using Random Forest and Neural network, outperformed the proposed Benchmark, improving the results by 4.5%.

Keywords: Data Mining, Machine Learning, Dengue, Regression, Artificial Neural Networks

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