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Publicação
Understanding Risk Factors of Post-Stroke Mortality
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| 1.11 MB | Adobe PDF |
Resumo(s)
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.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Risk Factors Analysis Stroke Mortality Machine Learning modified Rankin Scale Portugal SDG 3 - Good health and well-being