Publicação
Understanding Risk Factors of Post-Stroke Mortality
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | António, Nuno Miguel da Conceição | |
| dc.contributor.advisor | Marreiros, Ana Maria Duarte Inácio | |
| dc.contributor.author | Castro, David de Jesus Cardoso Pinheiro de | |
| dc.date.accessioned | 2024-11-07T14:52:49Z | |
| dc.date.embargo | 2026-10-29 | |
| dc.date.issued | 2024-10-29 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics | pt_PT |
| dc.description.abstract | Stroke is one of the leading causes of death worldwide. Understanding the risk factors for post-stroke mortality is crucial for improving patient outcomes. This study analyzes and predicts post-stroke mortality using the modified Rankin Scale (mRS), a functional neurological evaluation scale. Several machine learning models were developed and assessed using a dataset of 332 stroke patients from Hospital de Faro, Portugal, from 2016 to 2018. The Random Forest model outperformed others, achieving an accuracy of 98.5% and a recall of 91.3. Twenty-four risk factors were identified, with stroke severity (mRS) as the most critical. These findings provide healthcare professionals valuable tools for early identification and intervention for high-risk stroke patients, enabling informed decision-making and customized treatment plans. This research advances healthcare predictive analytics, offering a precise mortality prediction model and a comprehensive analysis of risk factors, potentially improving clinical outcomes and reducing mortality rates. Future applications could extend to patient monitoring and management across various medical conditions. | pt_PT |
| dc.identifier.tid | 203776992 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/174766 | |
| dc.language.iso | por | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Risk Factors Analysis | pt_PT |
| dc.subject | Stroke | pt_PT |
| dc.subject | Mortality | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | modified Rankin Scale | pt_PT |
| dc.subject | Portugal | pt_PT |
| dc.subject | SDG 3 - Good health and well-being | pt_PT |
| dc.title | Understanding Risk Factors of Post-Stroke Mortality | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Métodos Analíticos para a Gestão | pt_PT |