2015
Brandão, A.; Pereira, E.; Portela, F.; Santos, M.; Abelha, A.; Machado, J.
Predicting the risk associated to pregnancy using Data Mining Proceedings Article
Em: S., Filipe J. Filipe J. Loiseau (Ed.): pp. 594-601, SciTePress, 2015, ISBN: 9789897580741, (cited By 12; Conference of 7th International Conference on Agents and Artificial Intelligence, ICAART 2015 ; Conference Date: 10 January 2015 Through 12 January 2015; Conference Code:112667).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Competitive intelligence; Decision support systems; Decision trees; Digital storage; Obstetrics; Support vector machines, Classification tasks; Data mining models; Generalized linear model; Intelligent decision support systems; Real environments; Technology acceptance; Three different techniques; Voluntary interruption of pregnancy, Data mining
@inproceedings{Brandão2015594,
title = {Predicting the risk associated to pregnancy using Data Mining},
author = {A. Brandão and E. Pereira and F. Portela and M. Santos and A. Abelha and J. Machado},
editor = {Filipe J. Filipe J. Loiseau S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943227689&doi=10.5220%2f0005286805940601&partnerID=40&md5=1a23bacaf6154af6a890cd1fcc2bfafa},
doi = {10.5220/0005286805940601},
isbn = {9789897580741},
year = {2015},
date = {2015-01-01},
journal = {ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings},
volume = {2},
pages = {594-601},
publisher = {SciTePress},
abstract = {Woman willing to terminate pregnancy should in general use a specialized health unit, as it is the case of Maternidade Júlio Dinis in Porto, Portugal. One of the four stages comprising the process is evaluation. The purpose of this article is to evaluate the process of Voluntary Termination of Pregnancy and, consequently, identify the risk associated to the patients. Data Mining (DM) models were induced to predict the risk in a real environment. Three different techniques were considered: Decision Tree (DT), Support Vector Machine (SVM) and Generalized Linear Models (GLM) to perform the classification task. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was applied to drive this work. Very promising results were obtained, achieving a sensitivity of approximately 93%.},
note = {cited By 12; Conference of 7th International Conference on Agents and Artificial Intelligence, ICAART 2015 ; Conference Date: 10 January 2015 Through 12 January 2015; Conference Code:112667},
keywords = {Artificial intelligence; Competitive intelligence; Decision support systems; Decision trees; Digital storage; Obstetrics; Support vector machines, Classification tasks; Data mining models; Generalized linear model; Intelligent decision support systems; Real environments; Technology acceptance; Three different techniques; Voluntary interruption of pregnancy, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}