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Doctoral Thesis
DOI
10.11606/T.98.2014.tde-15072014-094917
Document
Author
Full name
José Alves Ferreira
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2014
Supervisor
Committee
Nicolosi, Denys Emilio Campion (President)
Melo, Marco Antonio Assis de
Moreira, Dalmo Antonio Ribeiro
Piegas, Leopoldo Soares
Rezende, Solange Oliveira
Title in Portuguese
Data mining em banco de dados de eletrocardiograma
Keywords in Portuguese
Apriori
Cardiologia
Data mining
Eletrocardiograma
KDD
Regras de associação
Abstract in Portuguese
Neste estudo, foi proposta a exploração de um banco de dados, com informações de exames de eletrocardiogramas (ECG), utilizado pelo sistema denominado Tele-ECG do Instituto Dante Pazzanese de Cardiologia, aplicando a técnica de data mining (mineração de dados) para encontrar padrões que colaborem, no futuro, para a aquisição de conhecimento na análise de eletrocardiograma. A metodologia proposta permite que, com a utilização de data mining, investiguem-se dados à procura de padrões sem a utilização do traçado do ECG. Três pacotes de software (Weka, Orange e R-Project) do tipo open source foram utilizados, contendo, cada um deles, um conjunto de implementações algorítmicas e de diversas técnicas de data mining, além de serem softwares de domínio público. Regras conhecidas foram encontradas (confirmadas pelo especialista médico em análise de eletrocardiograma), evidenciando a validade dessa metodologia.
Title in English
Data mining in electrocardiogram databases
Keywords in English
Apriori
Association rules
Cardiology
Data mining
Electrocardiogram.
KDD
Abstract in English
In this study, the exploration of electrocardiograms (ECG) databases, obtained from a Tele-ECG System of Dante Pazzanese Institute of Cardiology, has been proposed, applying the technique of data mining to find patterns that could collaborate, in the future, for the acquisition of knowledge in the analysis of electrocardiograms. The proposed method was to investigate the data looking for patterns without the use of the ECG traces. Three Data-mining open source software packages (Weka, Orange and R - Project) were used, containing, each one, a set of algorithmic implementations and various data mining techniques, as well as being a public domain software. Known rules were found (confirmed by medical experts in electrocardiogram analysis), showing the validity of the methodology.
 
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TeseJoseFerreira.pdf (2.31 Mbytes)
Publishing Date
2014-09-01
 
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