# Random Forest-Based Prediction of Stroke Outcome

arXiv: https://arxiv.org/abs/2402.00638 · HTML: https://mlsanity.com/paper/2402.00638/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 55 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Data-driven Advice for Applying Machine Learning to Bioinformatics Problems](https://mlsanity.com/paper/1708.05070/) (2017)

## Built on by (most cited first, 1 of 1)

- [Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder](https://mlsanity.com/paper/2609.22113/) (2026)

## Abstract (arXiv)

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.