Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/35675
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Abreu, Pedro Manuel Henriques da Cunha | - |
dc.contributor.author | Andrade, Bruno Filipe Aveleira | - |
dc.date.accessioned | 2017-01-13T15:45:57Z | - |
dc.date.available | 2017-01-13T15:45:57Z | - |
dc.date.issued | 2015-09-24 | - |
dc.identifier.uri | https://hdl.handle.net/10316/35675 | - |
dc.description | Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra | pt |
dc.description.abstract | Breast Cancer (BC) is the second most frequently diagnosed cancer and the fth cause of cancer mortality worldwide. Among women, it is the leading cause of cancer deaths, with more than 500 000 registered deaths in 2012, and Portugal also re ects that reality. Survival prediction plays a crucial role in diseases with associated high mortality rates, since it has the power to help clinicians to de ne each patient's prognosis, thus allowing to personalize the corresponding treatments. Particularly for BC, prognosis is related to the patterns of recurrence (cancer that reappears after treatment), and it even di ers depending on the local involved. This work analyses the data of a cohort of 97 patients, with a total of 27 characteristics, more than 50% of them incomplete. Therefore, the rst step is to handle Missing Data (Imputation or Deletion), to perform Classi cation afterwards. The purpose is to study the prognostic factors that de ne recurrence of female BC, to try to build a model that accurately predicts recurrence patterns, which would create the possibility of more targeted treatments. The application of machine learning algorithms to the prediction of recurrence in di erent sites seems to be a novel application of these methodologies, and the results can lead the way to a better understanding of the pathways of BC recurrence. | pt |
dc.language.iso | eng | pt |
dc.rights | openAccess | pt |
dc.subject | Women Breast Cancer | pt |
dc.title | Prediction Model for Women Breast Cancer Recurrence | pt |
dc.type | masterThesis | pt |
degois.publication.location | Coimbra | pt |
degois.publication.title | Prediction Model for Women Breast Cancer Recurrence | por |
dc.date.embargo | 2015-09-24 | * |
dc.identifier.tid | 201537680 | pt |
thesis.degree.grantor | 00500::Universidade de Coimbra | pt |
thesis.degree.name | Mestrado em Engenharia Informática | - |
uc.degree.grantorUnit | 0501 - Faculdade de Ciências e Tecnologia | por |
uc.rechabilitacaoestrangeira | no | pt |
uc.date.periodoEmbargo | 0 | pt |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.openairetype | masterThesis | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | Com Texto completo | - |
crisitem.advisor.orcid | 0000-0002-9278-8194 | - |
Appears in Collections: | UC - Dissertações de Mestrado FCTUC Eng.Informática - Teses de Mestrado |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Prediction Model for Women Breast Cancer Recurrence.pdf | 421.38 kB | Adobe PDF | View/Open |
Page view(s) 50
357
checked on May 7, 2024
Download(s) 50
694
checked on May 7, 2024
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.