Key result
Random Forest machine learning achieves 98% accuracy classifying normal and pathological ECG signals.
Why the study?
Conventional ECG processing equipment is costly and restricts availability in resource-limited contexts, necessitating automated classification techniques to analyze vast amounts of ECG data effectively.
A Random Forest machine learning algorithm with advanced feature extraction achieved 98% accuracy in classifying ECG signals into five arrhythmia categories.
Authors
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Supports ML-based ECG classification development; leaves open prospective clinical validation and integration.
Subba et al. (2024) studied Arrhythmia (n=47). Random Forest with Fast Fourier Transform feature extraction vs. Decision Tree, Logistic Regression, SVM, KNN was evaluated on Classification accuracy. A Random Forest machine learning algorithm with Fast Fourier Transform feature extraction achieved an overall accuracy of 98% in classifying ECG signals into normal and pathological classes.
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