Random Forest-Based Prediction of Stroke Outcome

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 stro...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: 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, Iglesias-Rey, Ramon
Format: Article
Language:English
Subjects:
Algorithms
Hemorrhage
Machine learning
Medical imaging
Mortality
Prediction models
Predictions
Random variables
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2402.00638