Asymmetric Objective Measures Applied to Filter Association Rules Networks

Rezende

et al. 2020

RITA

Self Cite

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.

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Section: Regras De Associac ¸ãOunclassified

Section: Redes De Regras De Associac ¸ãOunclassified

Section: Redes De Regras De Associac ¸ãO Filtradasunclassified

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

Silva

Rezende

et al. 2020

RITA

Self Cite

Identification of factors related to complications in myocardial revascularization surgery: an approach with multi-target association rules networks

Neto²,

Amato³

et al. 2022

RSD

Myocardial revascularization surgery is one of the recommended approaches for the treatment of chronic coronary disease. Several complications related to mortality, sequelae, length of stay, and hospital costs are also associated with this procedure. Death rates and complications depend on the characteristics of each patient. Knowing the factors related to hospital mortality and complications is paramount to improving outcomes. Association Rules Mining may support the discovery of those factors. In this work we propose a new approach, called Multi-target Association Rules Network (MTARN), to analyze association rules based on networks with a simultaneous focus on two parameters. The use of association rules networks aids the analysis of a high number of association rules and the multi-target strategy allows a complete exploration, explaining which factors directly influence the analyzed set. We evaluated our approach in conjunction with domain experts and compared it to two other approaches: Decision Trees and Filtered-ARNs, a single target approach based on networks for pattern visualization. The results indicates that MTARNs optimize the discovery of knowledge directly linked to complication and death factors in patients undergoing coronary artery bypass grafting. These parameters may be used in the construction of an intelligent monitoring system to aid myocardial revascularization patients.

Utilizando Inteligência Artificial Explicável para formação de perfil de jogadores no Cartola FC

Cruz

Sousa²,

2022

CONJ

O Cartola FC é um fantasy game, onde é possível realizar escalações em um time virtual usando como base os jogadores e técnicos de futebol da Série A do campeonato brasileiro. Existe uma dificuldade em obter bons resultados nas escalações pela volatilidade dos jogos e desempenho dos jogadores. O trabalho visa extrair e validar um modelo de perfil de jogador para a escalação no Cartola FC, utilizando técnica de Inteligência Artificial Explicável. Para criar o modelo foi usado a base de dados disponibilizada pela API da Globo, utilizando técnica de Redes de Regras de Associação Filtradas como principal meio de estruturar e entender os padrões da base de dados.