António, Nuno Miguel da ConceiçãoMarreiros, Ana Maria Duarte InácioCastro, David de Jesus Cardoso Pinheiro de2024-11-072024-10-29http://hdl.handle.net/10362/174766Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsStroke 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.porRisk Factors AnalysisStrokeMortalityMachine Learningmodified Rankin ScalePortugalSDG 3 - Good health and well-beingmaster thesis203776992