<ul><li>A novel hybrid architecture and feature-selection synergy was developed to advance tabular stroke modeling.</li><li>The machine-learning framework predicted strokes using demographic, lifestyle, and clinical variables with a high accuracy rate of 97.2% and an F1-score of 97.15%.</li><li>The framework involved detailed exploratory data analysis, rigorous data preprocessing, feature selection, and optimization of algorithms like Random Forest, XGBoost, LightGBM, and support-vector classifier.</li><li>The study highlights the potential of converting low-cost tabular data into a clinical-grade stroke-risk assessment tool through advanced modeling techniques.</li></ul>

Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy

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