Machine learning based cardiac care analysis using electro cardio graphs
Keywords:
ECG classification, machine learning, cardiovascular disease, real-time monitoring, dimensionality reductionAbstract
Accurate classification of electrocardiogram (ECG) signals is crucial for early detection and management of cardiovascular disorders. With the rise of wearable healthcare technology and increasing volumes of ECG data, the integration of advanced machine learning (ML) techniques has become essential. This study investigates the performance of various ML classifiers—SVC, Random Forest, XGBoost, and Linear SVC—for ECG classification using the PTB Diagnostic ECG Database. To improve predictive accuracy, metaheuristic optimization algorithms, including Enhanced AEO, LevyJA, JADE, and OriginalJA, were employed to fine-tune hyperparameters of the XGBoost model. Principal Component Analysis (PCA) was used for dimensionality reduction, and model performance was evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that the hybrid XGBoost-JADE model achieved superior classification performance with an F1-score of 0.8742, surpassing baseline models and existing literature benchmarks. This work also addresses real-time applicability in resource-constrained environments by discussing strategies for computational efficiency. The findings highlight the potential of metaheuristically optimized machine learning frameworks in enhancing automated ECG interpretation systems.
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