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| Hauptverfasser: | , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2402.00638 |
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| _version_ | 1866914663272284160 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00638 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Random Forest-Based Prediction of Stroke Outcome 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 Machine Learning 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. |
| title | Random Forest-Based Prediction of Stroke Outcome |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.00638 |