Fuzzy Modeling Built Through a Data Mining Process

Wilges, Beatriz; Mateus, Gustavo Pereira; Nassar, Sílvia Modesto; Bastos, Rogério Cid

doi:10.1109/tla.2012.6187607

Cited by 3 publications

(2 citation statements)

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“…Neste trabalho, todos os classificadores testados são baseados em Árvores de Decisão, pois segundo Wilges et al 2012 [13] esses são os algoritmos mais utilizados no processo de DCBD.…”

Section: Trabalhos De Influênciaunclassified

Evaluation of Classifiers Based on Decision Tree for Learning Medical Claim Process

Araújo

Santana

Neto

2015

IEEE Latin Am. Trans.

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Brazil has one of the largest private healthcare markets in the world. However, it appears that many medical procedures are being carried out without need, generating unnecessary costs on businesses and making the service offered more expensive. The medical claim process is a control mechanism used by health insurance companies to minimize the waste of resources through blockage of procedures that were requested wrongly. However, in order to realize an efficient medical claim process it needs a medical reviewer 24 hours a day and this generates a high cost. In this work, we followed the initial stages of the process of Knowledge Discovery Database in order to improve the quality of the data of a health insurance company. Then, we compared classification techniques based in Decision Tree in order to learn the medical reviewer behaviorprofessionals who assess if the medical requests should or not should be authorized. For the generation of knowledge, we used a database from a nonprofitable health insurance company containing records collected since the year 2007. Promising experimental results are present.

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“…Neste trabalho, todos os classificadores testados são baseados em Árvores de Decisão, pois segundo Wilges et al 2012 [13] esses são os algoritmos mais utilizados no processo de DCBD.…”

Section: Trabalhos De Influênciaunclassified

Evaluation of Classifiers Based on Decision Tree for Learning Medical Claim Process

Araújo

Santana

Neto

2015

IEEE Latin Am. Trans.

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“…Studies by Mateus et al (2011) already pointed out the need to find an adaptive module for individual students in a VLE according to their own performance, and a diffuse modeling for the VLE in relation to the data obtained regarding class performance results have already been detailed in studies by Wilges et al (2012). Besides, other papers by Mateus et al (2010) had already tried to recognize student profile through Multi-Agent System (MAS) and fuzzy logic.…”

Section: Fuzzy Agent (Fa) Implementationmentioning

confidence: 99%

An Animated Pedagogical Agent supporting student learning using performance analysis from a Fuzzy Agent

Mateus¹,

Wilges²,

Diniz³

et al. 2014

Anais Do XXV Simpósio Brasileiro De Informática Na Educação (SBIE 2014)

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Abstract. This paper presents a framework made up of agents that promote monitoring of the learning process in a Virtual Learning Environment (VLE)On the other hand. From the results of student performance in the VLE it was possible to perform the mapping of performance on formal tests. Having this mapping it is possible to evaluate whether the decision-making from the Fuzzy Agent (FA) is consistent with the intervention that the interface agent performs. The interface agent is an Animated Pedagogical Agent (APA) which has a specific behavior according to student performance, it is intended to transmit the messages to the student so it can track and monitor the whole learning process in the Virtual Learning Environment. General InformationMany studies present an interest in how to best guide students while using a Virtual Learning Environment (VLE). Van, Erik and Schepers's studies (2008) show evidence that the success of a VLE depended on the acceptance and usage of the e-learning system. In addition, they also highlighted the fact that education program managers should not worry exclusively about the basic system project, but should also deal explicitly with the individual differences between each VLE user.It was also observed in Chou and Liu's work (2005) that web-based technology has a positive impact in learning. In their studies, they presented data regarding the efficacy of a VLE in relation to traditional, face-to-face teaching method. Four critical factors were also identified, according to the authors: performance increase, higher teaching efficiency, higher student satisfaction levels, and learning.Similar studies aim to find constructs in student-oriented teaching on web-based systems. Ke and Kwak (2013)

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