We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3 months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.
@article{arxiv.2402.00638,
title = {Random Forest-Based Prediction of Stroke Outcome},
author = {Carlos Fernandez-Lozano and Pablo Hervella and Virginia Mato-Abad and Manuel Rodriguez-Yanez and Sonia Suarez-Garaboa and Iria Lopez-Dequidt and Ana Estany-Gestal and Tomas Sobrino and Francisco Campos and Jose Castillo and Santiago Rodriguez-Yanez and Ramon Iglesias-Rey},
journal= {arXiv preprint arXiv:2402.00638},
year = {2024}
}