1. Random Forest-Based Prediction of Stroke Outcome
- Author
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Fernandez-Lozano, Carlos, Hervella, Pablo, Mato-Abad, Virginia, Rodriguez-Yanez, Manuel, Suarez-Garaboa, Sonia, Lopez-Dequidt, Iria, Estany-Gestal, Ana, Sobrino, Tomas, Campos, Francisco, Castillo, Jose, Rodriguez-Yanez, Santiago, and Iglesias-Rey, Ramon
- Subjects
Computer Science - Machine Learning - Abstract
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., Comment: 12 pages, 5 figures
- Published
- 2024
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