Key result
Machine learning algorithms undergo comprehensive performance review for ECG-based heart disease classification.
Why the study?
Heart disease poses a major global threat, and early and accurate identification of abnormal cardiac signals through ECG analysis remains challenging.
This comprehensive review provides an indispensable resource on the current state-of-the-art machine learning techniques for ECG-based heart disease classification.
Authors
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Extends understanding of ensemble methods for ECG classification; leaves open prospective clinical validation before adoption.
Gour et al. (2023) conducted a review in Heart disease. ECG-based heart disease classification techniques was evaluated on Classification accuracy. This comprehensive review evaluates the performance of various machine learning algorithms, including support vector machines and neural networks, for ECG-based heart disease classification.
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