Automatic identification of knowledge related to dengue cases in the state of Piauí in public databases using Filtered-Association Rules Networks

Joan Davi Santos Silva, Jâina Carolina Meneses Calçada, Solange Oliveira Rezende, Dario Brito Calçada


Dengue is an endemic disease in Brazil since the 1980s and since 1996 in Piau ́ı. The number of cases increases each year, with the incidence of more severe symptoms. This research aimed to evaluate the use of an automatic knowledge identification technique in factors related to the number of dengue occurrences. We built a dataset formed by data available in the Information System for Notifiable Diseases (SINAN) and meteorological data of the municipalities of the coastal plain of Piau ́ı. The technique used was that of Filtered Association Rules Networks, which allows visual analysis of knowledge through the use of network structures and rules filtering. As a main result, we confirmed the understanding that the most significant number of cases occurs in May, as it is the moment when the rainfall indexes are decreasing, besides that socio-cultural and race factors do not interfere in the identification of the population of higher risk. This research presents the innovation of the use of a computational technique of automatic knowledge discovery that can assist in the elaboration of prevention actions by epidemiological surveillance.


Association Rules;Dengue;Epidemiological surveillance;Knowledge Discovery;Networks

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