Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/62777
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Cordeiro, João Rala | por |
dc.contributor.author | Postolache, Octavian | por |
dc.contributor.author | Ferreira, João C. | por |
dc.date.accessioned | 2019-12-21T13:03:41Z | - |
dc.date.available | 2019-12-21T13:03:41Z | - |
dc.date.issued | 2019-12-12 | - |
dc.identifier.citation | Cordeiro, J.R.; Postolache, O.; Ferreira, J.C. Child’s Target Height Prediction Evolution. Appl. Sci. 2019, 9, 5447. | por |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://hdl.handle.net/1822/62777 | - |
dc.description.abstract | This study is a contribution for the improvement of healthcare in children and in society generally. This study aims to predict children’s height when they become adults, also known as “target height”, to allow for a better growth assessment and more personalized healthcare. The existing literature describes some existing prediction methods, based on longitudinal population studies and statistical techniques, which with few information resources, are able to produce acceptable results. The challenge of this study is in using a new approach based on machine learning to forecast the target height for children and (eventually) improve the existing height prediction accuracy. The goals of the study were achieved. The extreme gradient boosting regression (XGB) and light gradient boosting machine regression (LightGBM) algorithms achieved considerably better results on the height prediction. The developed model can be usefully applied by pediatricians and other clinical professionals in growth assessment. | por |
dc.description.sponsorship | This work has been partially supported by Fundação para a Ciência e Tecnologia Project UID/EEA/50008/2019 and Instituto de Telecomunicações. | por |
dc.language.iso | eng | por |
dc.publisher | Multidisciplinary Digital Publishing Institute | por |
dc.relation | UID/EEA/50008/2019 | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | child height prediction | por |
dc.subject | growth assessment | por |
dc.subject | child personalized medicine | por |
dc.subject | data mining | por |
dc.subject | XGB-Extreme Gradient Boosting Regression | por |
dc.subject | LGBM-LightGradient Boosting Machine Regression | por |
dc.title | Child’s target height prediction evolution | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/9/24/5447 | por |
oaire.citationIssue | 24 | por |
oaire.citationVolume | 9 | por |
dc.date.updated | 2019-12-20T14:10:31Z | - |
dc.identifier.doi | 10.3390/app9245447 | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Applied Sciences | por |
oaire.version | VoR | por |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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applsci-09-05447.pdf | 3,53 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons