<ul><li>Heart disease remains a leading cause of mortality, necessitating accurate predictive models.</li><li>Nine machine learning algorithms were applied, including XGBoost, logistic regression, decision tree, random forest, KNN, SVM, NB Gaussian, adaptive boosting, and linear regression.</li><li>Feature selection techniques were used to refine the models and enhance performance and interpretability.</li><li>XGBoost demonstrated exceptional performance with 99% accuracy, precision, F1-score, 98% recall, and 100% ROC AUC.</li></ul>

Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques

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