Understanding risk factors of post-stroke mortality

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 as the most critical. These findings provide healthcare professionals with 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. • Machine learning models identify 24 critical risk factors for post-stroke mortality. • Random Forest model achieved 98.5% accuracy in predicting post-stroke mortality. • SHAP values provide clear, interpretable insights into individual risk factors. • The study offers tools for early intervention and customized treatment in stroke care.