Random Forest-Based Prediction of Stroke Outcome
This work addresses stroke outcome prediction for clinicians and patients, but it is incremental as it applies an existing method (Random Forest) to a new dataset without major methodological innovation.
The researchers tackled predicting stroke patient mortality and morbidity three months after admission by developing a Random Forest model using clinical, biochemical, and neuroimaging data from a European hospital, concluding that RF is effective for this long-term outcome prediction.
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.