Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review - da Silva Neto, Sebastião Rogério; Tabosa Oliveira, Thomás; Teixeira, Igor Vitor; Aguiar de Oliveira, Samuel Benjamin; Souza Sampaio, Vanderson; Lynn, Theo; Endo, Patricia Takako (PLOS Neglected Tropical Diseases, 2022)
Author summary Neglected tropical diseases (NTDs) primarily affect the poorest populations, often living in remote, rural areas, urban slums or conflict zones. Arboviruses are a significant NTD category spread by mosquitoes. Dengue, Chikungunya, and Zika are three arboviruses that affect a large proportion of the population in Latin and South America. The clinical diagnosis of these arboviral diseases is a difficult task due to the concurrent circulation of several arboviruses which present similar symptoms and, sometimes, inaccurate test results. In this paper, we present the state of the art of studies investigating the automatic classification of arboviral diseases based on Machine Learning (ML) and Deep Learning (DL) models. Results show that current research is focused on the classification of Dengue, primarily using tree-based ML algorithms. The use of an efficient clinical decision support system for arboviral diseases can improve the quality of the entire clinical process, thus increasing the accuracy of the diagnosis and the associated treatment. It should help physicians in their decision-making process and, consequently, improve the use of resources and the patient’s quality of life.