Stacking ensemble of Random Forest, XGBoost, LightGBM and SVC with logistic regression meta-learner reaches 97.2% accuracy and 97.15% F1-score on stroke prediction from tabular data, outperforming single models.
Abousaber, A novel explainable attention-based meta-learning frame- work for imbalanced brain stroke prediction (2025)
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Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy
Stacking ensemble of Random Forest, XGBoost, LightGBM and SVC with logistic regression meta-learner reaches 97.2% accuracy and 97.15% F1-score on stroke prediction from tabular data, outperforming single models.