Descoberta de regras de associação com algoritmo Tertius

Auteurs

DOI :

https://doi.org/10.33837/msj.v1i7.334

Mots-clés :

Mineração de Dados, Regras de Associação, Tertius.

Résumé

O trabalho descreve os experimentos do processo de definição das regras de associação com base nas regras do desafio PhysioNet de 2012. O desafio disponibilizou uma base com 4000 pacientes com informações das últimas 48 horas de permanência em uma UTI. Na descoberta das regras de associação, agrupamos os registros por paciente e calculamos a média aritmética de cada atributo temporal. Para realizar os treinamentos utilizamos somente as primeiras 24 horas agrupadas de 6 em 6 horas. Utilizamos o algoritmo Tertius para gerar automaticamente as regras em conjunto com a ferramenta Weka.

Bibliographies de l'auteur

Norton Coelho Guimarães, IF Goiano

Mestre em Ciência da Computação pela UFG/INF (2015), especialista em Orientação a Objetos e Internet pela Faculdade Anhanguera - GO (2007) e graduado em Análise de Sistemas pela UNIVERSO campus Goiânia (2000). Desde 2009, docente no IF Goiano, na área de Ciência da Computação, com atuação no ensino, pesquisa e extensão.

Rosângela da Silva Nunes, Universidade Federal de Goiás

Analista de Sistemas no CERCOMP/UFG, Mestre em Engenharia Elétrica.

Références

ANDRADE, A. A. d. Desenvolvimento de sistema especialista com operacionalidade de aprendizado para operar em tempo real com sistemas industriais automatizados. PhD thesis, Escola Politécnica - USP, São Paulo, Brasil, abr 2007.

BERA, D.; NAYAK, M. Mortality risk assessment for icu patients using logistic regression. In: Computing in Cardiology (CinC), 2012, p. 493–496, 2012.

BOSNJAK, A.; MONTILLA, G. Predicting mortality of ICU patients using statistics of physiological variables and support vector machines. In: Computing in Cardiology (CinC), 2012, p. 481–484, 2012.

CITI, L.; BARBIERI, R. Physionet 2012 challenge: Predicting mortality of icu patients using a cascaded svm-glm paradigm. In: Computing in Cardiology (CinC), 2012, p. 257–260, 2012.

FLACH, P. A.; LACHICHE, N. Confirmation-guided discovery of first-order rules with tertius. Machine Learning, 42:61–95, 1999.

GOLDBERGER, A. L.; AMARAL, L. A. N.; GLASS, L.; HAUSDORFF, J. M.; IVANOV, P. C.; MARK, R. G.; MIETUS, J. E.; MOODY, G. B.; PENG, C.-K.; STANLEY, H. E. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23):e215–e220, 2000 (June 13). Circulation Electronic Pages: http://circ.ahajournals.org/cgi/content/full/101/23/e215 PMID:1085218; doi: 10.1161/01.CIR.101.23.e215.

HALL, M.; FRANK, E.; HOLMES, G.; PFAHRINGER, B.; REUTEMANN, P.; Witten, I. H. The weka data mining software: an update. SIGKDD Explor. Newsl., 11(1):10–18, Nov. 2009.

HAMILTON, S.; HAMILTON, J. Predicting in-hospital-death and mortality percentage using logistic regression. In: Computing in Cardiology (CinC), 2012, p. 489–492, 2012.

JOHNSON, A.; DUNKLEY, N.; MAYAUD, L.; TSANAS, A.; Kramer, A.; Clifford, G. Patient specific predictions in the intensive care unit using a bayesian ensemble. In: Computing in Cardiology (CinC), 2012, p. 249–252, 2012.

LEE, C.; ARZENO, N.; HO, J.; VIKALO, H.; GHOSH, J. An imputation-enhanced algorithm for icu mortality prediction. In: Computing in Cardiology (CinC), 2012, p. 253–256, 2012.

MACAS, M.; KUZILEK, J.; ODSTRCILIK, T.; HUPTYCH, M. Linear bayes classification for mortality prediction. In: Computing in Cardiology (CinC), 2012, p. 473–476, 2012.

MCMILLAN, S.; CHIA, C.-C.; VAN ESBROECK, A.; RUBINFELD, I.; SYED, Z. Icu mortality prediction using time series motifs. In: Computing in Cardiology (CinC), 2012, p. 265– 268, 2012.

POLLARD, T.; HARRA, L.; WILLIAMS, D.; HARRIS, S.; MARTINEZ, D.; FONG, K. 2012 physionet challenge: An artificial neural network to predict mortality in icu patients and application of solar physics analysis methods. In: Computing in Cardiology (CinC), 2012, p. 485–488, 2012.

SAEED, M.; VILLARROEL, M.; REISNER, A.; CLIFFORD, G.; LEHMAN, L.; MOODLY, G.; HELDT, T.; KYAW, T.; MOODY, B.; MARK, R. Multiparameter intelligent monitoring in intensive care ii (mimic ii): A public-acess intensive care unit database. Critical Care Medicine, 2011.

SCHEFFER, T. Finding association rules that trade support optimally against confidence. Intell. Data Anal., 9(4):381–395, July 2005.

XIA, H.; DALEY, B.; PETRIE, A.; ZHAO, X. A neural network model for mortality prediction in icu. In: Computing in Cardiology (CinC), 2012, p. 261–264, 2012.

XU, J.; LI, D.; ZHANG, Y.; DJULOVIC, A.; LI, Y.; ZENG, Y. Cinc challenge: Cluster analysis of multi-granular time-series data for mortality rate prediction. In: Computing in Cardiology (CinC), 2012, p. 497–500, 2012.

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Publiée

2018-03-18

Comment citer

Guimarães, N. C., & Nunes, R. da S. (2018). Descoberta de regras de associação com algoritmo Tertius. Multi-Science Journal, 1(7), 41–45. https://doi.org/10.33837/msj.v1i7.334

Numéro

Vol. 1 No 7 (2017)

Rubrique

Technical Communications

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