Random Forest-Based Prediction of Stroke Outcome
Carlos Fernandez-Lozano Pablo Hervella Virginia Mato-Abad Manuel Rodriguez-Yanez Sonia Suarez-Garaboa Iria Lopez-Dequidt Ana Estany-Gestal Tomas Sobrino Francisco Campos Jose Castillo Santiago Rodriguez-Yanez Ramon Iglesias-Rey
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.