Please use this identifier to cite or link to this item: https://www.arca.fiocruz.br/handle/icict/24809
Title: Evaluating the surveillance system for spotted fever in Brazil using machine-learning techniques
Authors: Lopez, Diego Montenegro
Mello, Flávio Luis de
Dias, Cristina Maria Giordano
Almeida, Paula
Araújo, Milton
Magalhaes, Monica Avelar
Gazeta, Gilberto Salles
Brasil, Reginaldo Peçanha
Affilliation: Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Doenças Parasitárias. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Referência Nacional em vetores das Riquetsioses. Rio de Janeiro, RJ, Brasil.
Universidade Federal do Rio de Janeiro. Departamento de Engenharia Eletrônica e de Computadores. Rio de Janeiro, RJ, Brasil.
Secretaria de Estado de Saúde do Rio de Janeiro. Rio de Janeiro, RJ, Brasil
Secretaria de Estado de Saúde do Rio de Janeiro. Rio de Janeiro, RJ, Brasil
Secretaria de Estado de Saúde do Rio de Janeiro. Rio de Janeiro, RJ, Brasil
Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnologia emSaúde. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Referência Nacional em vetores das Riquetsioses. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Doenças Parasitárias. Rio de Janeiro, RJ, Brasil.
Abstract: This work analyses the performance of the Brazilian spotted fever (SF) surveillance system in diagnosing and confirming suspected cases in the state of Rio de Janeiro (RJ), from 2007 to 2016 (July) using machine-learning techniques. Of the 890 cases reported to the Disease Notification Information System (SINAN), 11.7% were confirmed as SF, 2.9% as dengue, 1.6% as leptospirosis, and 0.7% as tick bite allergy, with the remainder being diagnosed as other categories (10.5%) or unspecified (72.7%). This study confirms the existence of obstacles in the diagnostic classification of suspected cases of SF by clinical signs and symptoms. Unlike man–capybara contact (1.7% of cases), man–tick contact (71.2%) represents an important risk indicator for SF. The analysis of decision trees highlights some clinical symptoms related to SF patient death or cure, such as: respiratory distress, convulsion, shock, petechiae, coma, icterus, and diarrhea. Moreover, cartographic techniques document patient transit between RJ and bordering states and within RJ itself. This work recommends some changes to SINAN that would provide a greater understanding of the dynamics of SF and serve as a model for other endemic areas in Brazil.
Keywords: public health
epidemiology
spotted fever
machine-learning
probabilistic neural networks
decision trees
keywords: Saúde pública
Epidemiologia
Febre manchada
aprendizagem mecânica
redes neurais probabilísticas
Decisão
Issue Date: 2017
Publisher: Frontiers Media
Citation: LOPEZ, Diego Montenegro; et al. Evaluating the surveillance system for spotted fever in Brazil using machine-learning techniques. Frontiers in Public Health, v.5, Article 323, 9p, Nov. 2017.
ISSN: 2296-2565
Copyright: open access
Appears in Collections:ICICT - Artigos de Periódicos
IOC - Artigos de Periódicos

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