Binary Models for Arboviruses Classification Using Machine Learning: A Benchmarking Evaluation

• DOI: 10.24251/hicss.2023.348
• Publication Date: 2024-02-16T15:38:35Z
• Authors: Sebastião Rogerio...; Thomás Tabosa; Leonides Medeiros...; Igor Vitor Teixeira; Sara Sadok; Vanderson De Souz...; Patricia Takako Endo
• Keywords: arbovirus; machine learning; classification; Decision Support for Healthcare Processes and Services; artificial intelligence; 004

Arboviral diseases are common worldwide. Infection with arboviruses can lead to serious health problems, even death in severe cases. Such health problems can be prevented by the early and correct detection of these arboviruses, but this is challenging due to the overlap of their symptoms. In this work, we benchmark different Machine Learning (ML) models to classify two types of arboviruses. We propose two distinct binary models: (i) a model to classify if the patient has arbovirus or another disease; and (ii) a model to classify if the patient has Dengue or Chikungunya. We configure and evaluate several ML models using hyperparameter optimization and feature selection techniques. The Random Forest and XGboost tree-based models present the best results with over 80% recall in the Chikungunya and Inconclusive classes.