Evaluating the surveillance system for spotted fever in Brazil using machine-learning techniques

dc.creatorLopez, Diego Montenegro
dc.creatorMello, Flávio Luis de
dc.creatorDias, Cristina Maria Giordano
dc.creatorAlmeida, Paula
dc.creatorAraújo, Milton
dc.creatorMagalhaes, Monica Avelar
dc.creatorGazeta, Gilberto Salles
dc.creatorBrasil, Reginaldo Peçanha
dc.date2018-02-12T16:12:35Z
dc.date2018-02-12T16:12:35Z
dc.date2017
dc.date.accessioned2023-09-27T00:14:42Z
dc.date.available2023-09-27T00:14:42Z
dc.identifierLOPEZ, 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.
dc.identifier2296-2565
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/24809
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8898897
dc.descriptionThis 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.
dc.formatapplication/pdf
dc.languageeng
dc.publisherFrontiers Media
dc.rightsopen access
dc.subjectSaúde pública
dc.subjectEpidemiologia
dc.subjectFebre manchada
dc.subjectaprendizagem mecânica
dc.subjectredes neurais probabilísticas
dc.subjectDecisão
dc.subjectpublic health
dc.subjectepidemiology
dc.subjectspotted fever
dc.subjectmachine-learning
dc.subjectprobabilistic neural networks
dc.subjectdecision trees
dc.titleEvaluating the surveillance system for spotted fever in Brazil using machine-learning techniques
dc.typeArticle


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