Explainable Machine Learning for the Prediction of Ischemic Stroke Subtypes in Young Adults in Jordan Using Clinical and Imaging Variables

Stroke is the leading cause of long-term disability in young adults aged 18–49 years, yet automated TOAST subtype classification from clinical data remains understudied, particularly in Jordan. Six machine learning algorithms (Random Forest, Gradient Boosting, Logistic Regression, SVM-RBF, XGBoost, LightGBM) were evaluated for five-class TOAST classification in 91 young adult ischemic stroke patients from Jordan University Hospital (2019–2023). Twelve binary clinical and imaging variables recorded by the neurology team were integrated with structured clinical data. A strict no-leakage nested cross-validation framework was applied, with all preprocessing within each training fold. XGBoost achieved the highest macro F1-score (0.575 ± 0.078) as the pre-specified primary metric; SVM (RBF) achieved the highest AUC-ROC (0.795 ± 0.071). Arterial stenosis and lacunar infarct were selected in all five cross-validation folds. SHAP analysis demonstrated pathophysiologically coherent class-specific feature contributions. All models outperformed a structured-variables-only baseline (F1 = 0.352). Integrating clinical and imaging variables substantially improves TOAST classification, with the primary value lying in process standardization and automation rather than novel etiologic discovery. External validation on larger multicenter cohorts is required before clinical deployment.