STACK-ENSEMBLE MODEL

Cardiovascular Diseases (CVDs) remain the paramount global health challenge, responsible for approximately 17.9 million deaths annually, with cardiac arrest representing a particularly critical and sudden event. Despite advancements in machine learning, accurate prediction is often hindered by highdimensional clinical data ("the curse of dimensionality") and the performance limitations of standalone classifiers. Consequently, this study aimed to develop a framework that integrates a novel Hybrid Recursive Feature Elimination-Firefly (RFE-Firefly) feature selection technique with a stacked ensemble classifier to enhance predictive accuracy. The study utilized a structured heart disease dataset from the Kaggle repository, subjecting it to rigorous pre-processing, including normalization and class balancing via the Synthetic Minority Over-sampling Technique (SMOTE). The proposed Hybrid RFEFirefly algorithm was implemented to optimize the feature space, successfully reducing the data to a parsimonious subset of seven critical features. For classification, a stacking ensemble model was constructed using five diverse base learners which are Decision Tree, Support Vector Machine, Logistic Regression, Random Forest, and XGBoost and unified by Random Forest as meta-learner to synthesize their predictive strengths. Experimental results demonstrated that the proposed Hybrid RFE-FA Stack Ensemble achieved a superior accuracy of 90.57% and an F1-score of 0.7619. This performance significantly outperformed individual base classifiers as well as ensembles using standalone feature selection methods. The study concludes that integrating hybrid feature selection strategy with an advance stack ensemble classifier effectively mitigates overfitting and provides a robust, highperformance tool for clinical decision-making in cardiac arrest detection.