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An analysis of malaria in the Brazilian Legal Amazon using divergent association rules

An analysis of malaria in the Brazilian Legal Amazon using divergent association rules

03/08/2020
Authors
Lais Baroni, Rebecca Salles, Samella Salles, Gustavo Guedes, Fabio Porto, Eduardo Bezerra, Christovam Barcellos, Marcel Pedroso, Eduardo Ogasawara

In data analysis, the mining of frequent patterns plays an important role in the discovery of associations and correlations between data. During this process, it is common to produce thousands of association rules (ARs), making the study of each one arduous. This problem weakens the process of finding useful information. There is a scientific effort to develop approaches capable of filtering interesting patterns, balancing the number of ARs produced with the goal of not being trivial and known by specialists. However, even when such approaches are adopted, the number of produced ARs can still be high. This work contributes by presenting Divergent Association Rules Approach (DARA), a novel approach for obtaining ARs that presents themselves in divergence with the data distribution. DARA is applied right after traditional approaches to filtering interesting patterns. To validate our approach, we studied the dataset related to the occurrence of malaria in the Brazilian Legal Amazon. The discovered patterns highlight that ARs brought relevant insights from the data. This article contributes both in the medical and computer science fields since this novel computational approach enabled new findings regarding malaria in Brazil.

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Dexl Members

Fabio Porto
Marcel de Moraes Pedroso

Institutions

LNCC
CEFET/RJ