2019
Loreto, P.; Peixoto, H.; Abelha, A.; Machado, J.
Predicting low birth weight babies through data mining Proceedings Article
Em: S., Adeli H. Rocha A. Costanzo (Ed.): pp. 568-577, Springer Verlag, 2019, ISSN: 21945357, (cited By 11; Conference of World Conference on Information Systems and Technologies, WorldCIST 2019 ; Conference Date: 16 April 2019 Through 19 April 2019; Conference Code:224789).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Classification (of information); Decision support systems; Health risks; Information systems; Information use; Medical imaging, CRISP-DM; Decision support system (dss); Gestational age; Health condition; Knowledge discovery in database; Low birth weights; Physical characteristics; Quality of life, Data mining
@inproceedings{Loreto2019568,
title = {Predicting low birth weight babies through data mining},
author = {P. Loreto and H. Peixoto and A. Abelha and J. Machado},
editor = {Adeli H. Rocha A. Costanzo S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065068373&doi=10.1007%2f978-3-030-16187-3_55&partnerID=40&md5=a44b920a5b272005f074e6603d35e795},
doi = {10.1007/978-3-030-16187-3_55},
issn = {21945357},
year = {2019},
date = {2019-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {932},
pages = {568-577},
publisher = {Springer Verlag},
abstract = {Low Birth Weight (LBW) babies have a high risk of developing certain health conditions throughout their lives that affect negatively their quality of life. Therefore, a Decision Support System (DSS) that predicts whether a baby will be born with LBW would be of great interest. In this study, six different Data Mining (DM) algorithms are tested for five different scenarios. The scenarios combine information about the mother’s physical characteristics and habits, and the gestation. Results are promising and the best model achieved a sensitivity of 91,4% and a specificity of 99%. Good results were also achieved without considering the gestational age, which showed that the use of DM might be a good alternative to the traditional medical imaging exams in the prediction of LBW early in the pregnancy. © Springer Nature Switzerland AG 2019.},
note = {cited By 11; Conference of World Conference on Information Systems and Technologies, WorldCIST 2019 ; Conference Date: 16 April 2019 Through 19 April 2019; Conference Code:224789},
keywords = {Artificial intelligence; Classification (of information); Decision support systems; Health risks; Information systems; Information use; Medical imaging, CRISP-DM; Decision support system (dss); Gestational age; Health condition; Knowledge discovery in database; Low birth weights; Physical characteristics; Quality of life, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}