<![CDATA[Arrhythmia is a cardiac rhythm disorder that can lead to severe complications, including heart failure and sudden cardiac death. Accurate electrocardiogram (ECG)-based arrhythmia detection remains challenging due to high-dimensional features and class imbalance. Therefore, this study aims to develop a two-stage machine learning approach for arrhythmia detection using Recursive Feature Elimination (RFE) and Extreme Gradient Boosting (XGBoost). The proposed approach performs binary classification to distinguish normal and arrhythmia conditions, followed by multi-class classification to identify arrhythmia subtypes. SMOTE is applied to address class imbalance, while Grid Search with cross-validation is used for hyperparameter optimization. Furthermore, the trained model is implemented in a web-based application for interactive prediction and visualization. Experimental results show that the optimized binary classification model achieves an accuracy of 0.89 and an F1-score of 0.87. Meanwhile, the multi-class classification model achieves an accuracy of 0.69 and a weighted F1-score of 0.66. The results indicate that the proposed approach performs effectively for binary arrhythmia detection. However, performance in multi-class classification remains limited due to imbalance and insufficient samples in several arrhythmia subtypes. This study contributes by proposing an integrated framework that combines Recursive Feature Elimination (RFE) for feature selection, SMOTE for imbalance handling, XGBoost with GridSearchCV-based hyperparameter optimization, and a two-stage classification approach for ECG-based arrhythmia detection and subtype classification. In addition, the proposed model is implemented in a web-based application to support interactive prediction and visualization. Overall, this study demonstrates the potential of integrating RFE, XGBoost, and SMOTE for ECG-based arrhythmia detection and practical web-based implementation.]]>
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