IETS-Stroke: An Imbalance-Aware, Explainable, and Threshold-Tuned Stacking Framework for Stroke Risk Screening with Cross-Dataset Validation

This research proposes IETS-Stroke, an imbalance-aware, explainable, and threshold-tuned stacking framework for stroke risk screening using public-health datasets. The framework combines imbalance handling, stacking ensemble learning, threshold optimisation, SHAP explainability, and BRFSS-to-NHANES external validation. It can be used as a screening-support approach to identify individuals who may require further clinical assessment, rather than as a diagnostic system.