Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/78003
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Neto, Cristiana | por |
dc.contributor.author | Ferreira, Diana | por |
dc.contributor.author | Ramos, José | por |
dc.contributor.author | Cruz, Sandro | por |
dc.contributor.author | Oliveira, Joaquim M. | por |
dc.contributor.author | Abelha, António | por |
dc.contributor.author | Machado, José Manuel | por |
dc.date.accessioned | 2022-05-30T11:22:51Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.citation | Neto, C. et al. (2022). Prediction Models for Coronary Heart Disease. In: Matsui, K., Omatu, S., Yigitcanlar, T., González, S.R. (eds) Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-86261-9_12 | por |
dc.identifier.isbn | 978-3-030-86260-2 | - |
dc.identifier.issn | 2367-3370 | - |
dc.identifier.uri | https://hdl.handle.net/1822/78003 | - |
dc.description.abstract | In the current days, it is known that a great amount of effort is being applied to improving healthcare with the use of Artificial Intelligence technologies in order to assist healthcare professionals in the decision-making process. One of the most important field in healthcare diagnoses is the identification of Coronary Heart Disease since it has a high mortality rate worldwide. This disease occurs when the heart’s arteries are incapable of providing enough oxygen-rich blood to the heart. Thus, this study attempts to develop Data Mining models, using Machine Learning algorithms, capable of predicting, based on patients’ data, if a patient is at risk of developing any kind of Coronary Heart Disease within the next 10 years. To achieve this goal, the study was conducted by the CRISP-DM methodology and using the RapidMiner software. The best model was obtained using the Decision Tree algorithm and with Cross-Validation as the sampling method, obtaining an accuracy of 0.884, an AUC value of 0.942 and an F1-Score of 0.881. | por |
dc.description.sponsorship | This work has been supported FCT—Fundação para a Ciência e Tecnologia (Portugal) within the Project Scope: UIDB/00319/2020. | por |
dc.language.iso | eng | por |
dc.publisher | Springer | por |
dc.relation | UIDB/00319/2020 | por |
dc.rights | restrictedAccess | por |
dc.title | Prediction models for coronary heart disease | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-86261-9_12 | por |
oaire.citationStartPage | 119 | por |
oaire.citationEndPage | 128 | por |
oaire.citationVolume | 327 LNNS | por |
dc.date.updated | 2022-05-30T11:11:49Z | - |
dc.identifier.doi | 10.1007/978-3-030-86261-9_12 | por |
dc.date.embargo | 10000-01-01 | - |
dc.identifier.eisbn | 978-3-030-86261-9 | - |
sdum.export.identifier | 11179 | - |
sdum.journal | Lecture Notes in Networks and Systems | por |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
Prediction Models for Coronary Heart Disease.pdf Acesso restrito! | 85,45 kB | Adobe PDF | Ver/Abrir |