Key result
Machine learning improves arrhythmia diagnosis performance and efficiency when paired with clinical expertise.
Why the study?
The usage of machine learning in detecting cardiac arrhythmia from ECG can help improve diagnostic performance and efficiency.
Machine learning can improve the performance and efficiency of ECG-based cardiac arrhythmia diagnosis when combined with clinical judgment.
May enhance ECG arrhythmia detection alongside expertise; leaves open need for prospective validation before practice change.
The testing and evaluation of cardiac arrhythmia requires the use of Electro cardio gram (ECG), a diagnostic tool. Cardiac arrhythmia refers to abnormalities in the rate of the heartbeat. It happens when the heart’s electrical signals are malfunctioning and the heart beats too quickly, too slowly, or irregularly. The usage of machine learning in detecting cardiac arrhythmia can help improve the performance and efficiency of diagnosis, but it is important to note that machine learning algorithms should always be used hand-to-hand with clinical expertise and judgment. Several factors, including excessive blood pressure, alcohol misuse, and genetics, can contribute to cardiac arrhythmia. If not attended it can be life-threatening and lead to death.
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Jyothirmai et al. (2023) conducted a review in Cardiac arrhythmia. Machine learning was evaluated. Machine learning algorithms can improve the performance and efficiency of cardiac arrhythmia diagnosis when used alongside clinical expertise.
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