Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability.

The random forest model robustly predicted stroke risk using demographic and clinical variables. Feature importance highlighted priorities like hypertension and diabetes for clinical monitoring and intervention. This could help enable data-driven stroke prevention strategies.