Mineração de Dados Aplicada para Diagnosticar Doenças Causadas pelo Vírus Limfotrópico: Uma Analise de Desempenho (Data Mining Applied to Diagnose Diseases Caused by Lymphotropic Virus: a Performance Analysis)

Fabricio de Souza Farias (fabriciosf@ufpa.br), Lamartine Vilar de Souza (lvsouza@ufpa.br), Rita Catarina Medeiros Sousa (ritasousa@iec.gov.br), Cezar Augusto Muniz Caldas (cezar_caldas@yahoo.com.br), Letícia Figueiredo Gomes (letícia.gomes@ics.ufpa.br), João Crisóstomo Weyl Albuquerque Costa (jweyl@ufpa.br)

Universidade Federal do Pará
This paper appears in: Revista IEEE América Latina

Publication Date: Jan. 2012
Volume: 10,   Issue: 1 
ISSN: 1548-0992

This paper proposes a new methodology to diagnose the rheumatology manifestations and HTLV-I-Associated Myelopathy/Tropical Spastic Paraparesis, or HAM/TSP, in patients who have Lymphotropic virus of T cells in Humans or HTLV of type I and II. Computational intelligence algorithms are used to classify HTLV patient carriers with or without the presence of rheumatology manifestations and of HAM / TSP. A benchmarking is performed among artificial neural intelligence, naïve bayes, Bayesian networks and decision tree to evaluate the most suitable technique for solving this application issue. The obtained results demonstrate the potential of the methodology on the helping non-specialist doctors to classify the patient with the disease suspicion.

Index Terms:
Computational Intelligence, Data-Mining, Neural Networks.   

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