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A Comparative Study of Machine Learning Techniques for Multi-Class Classification of Arboviral Diseases

2022; Frontiers Media; Volume: 2; Linguagem: Inglês

10.3389/fitd.2021.769968

2673-7515

Thomás Tabosa de Oliveira, Sebastião Rogério da Silva Neto, Igor Vitor Teixeira, Samuel Benjamin Aguiar de Oliveira, Maria Gabriela de Almeida Rodrigues, Vanderson de Souza Sampaio, Patrícia Takako Endo,

Zoonotic diseases and public health

Among the neglected tropical diseases (NTDs), arboviral diseases present a significant number of cases worldwide. Their correct classification is a complex process due to the similarity of symptoms and the lack of tests in Brazil countryside is a big challenge to be overcome. Given this context, this paper proposes a comparative study of machine learning techniques for multi-class classification of arboviral diseases, which considers three classes: DENGUE, CHIKUNGUNYA and OTHERS, and uses clinical and socio-demographic data from patients. Feature selection techniques were also used for selecting the best subset of attributes for each model. Gradient boosting machines presented the best result in the metrics and a good subset of attributes for daily usage by the physicians that resulted in a 76.58% recall on the CHIKUNGUNYA class.