Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/65743
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Reis, Rita | por |
dc.contributor.author | Peixoto, Hugo | por |
dc.contributor.author | Machado, José | por |
dc.contributor.author | Abelha, António | por |
dc.date.accessioned | 2020-06-23T14:10:02Z | - |
dc.date.available | 2020-06-23T14:10:02Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 2299-1093 | - |
dc.identifier.uri | https://hdl.handle.net/1822/65743 | - |
dc.description.abstract | Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%. | por |
dc.description.sponsorship | This work has been supported by Compete: POCI-01-0145-FEDER-007043 and FCT within the Project Scope UID/CEC/00319/2013. | por |
dc.language.iso | eng | por |
dc.publisher | De Gruyter Open | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147280/PT | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | por |
dc.subject | Health Information Systems | por |
dc.subject | Data Mining | por |
dc.subject | Classification Techniques | por |
dc.subject | Decision Support Systems | por |
dc.subject | Nutrition Evaluation | por |
dc.title | Machine learning in nutritional follow-up research | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.degruyter.com/view/journals/comp/7/1/article-p41.xml | por |
oaire.citationStartPage | 41 | por |
oaire.citationEndPage | 45 | por |
oaire.citationIssue | 1 | por |
oaire.citationVolume | 7 | por |
dc.identifier.doi | 10.1515/comp-2017-0008 | por |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Open Computer Science | por |
oaire.version | VoR | por |
Aparece nas coleções: | HASLab - Artigos em revistas internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Machine Learning in Nutritional Follow-up Research.pdf | 376,52 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons