Random Forest-Based Prediction of Stroke Outcome

Carlos Fernandez-Lozano, Pablo Hervella, Virginia Mato-Abad, Manuel Rodriguez-Yanez, Sonia Suarez-Garaboa +7 more
2/1/2024
cs.LG

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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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Cite this paper

@article{fernandezlozano2024random,
  title  = {Random Forest-Based Prediction of Stroke Outcome},
  author = {Carlos Fernandez-Lozano and Pablo Hervella and Virginia Mato-Abad and Manuel Rodriguez-Yanez and Sonia Suarez-Garaboa and Iria Lopez-Dequidt and Ana Estany-Gestal and Tomas Sobrino and Francisco Campos and Jose Castillo and Santiago Rodriguez-Yanez and Ramon Iglesias-Rey},
  year   = {2024},
  eprint = {2402.00638},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.00638v1}
}

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