Random Forest-Based Prediction of Stroke Outcome

• URL: http://arxiv.org/abs/2402.00638v1
• Date: Thu, 1 Feb 2024 14:54:17 GMT
• Title: Random Forest-Based Prediction of Stroke Outcome
• Authors: 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 and Ramon Iglesias-Rey
• Abstract summary: We generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3 months after admission. In conclusion, machine learning RF algorithms can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.
• Score: 7.090384254446659
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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