Key result
The dipper throated optimization algorithm combined with a K-nearest neighbors classifier achieved 99.8% accuracy in classifying cardiac arrhythmias from electrocardiogram data.
Why the study?
Traditional handcrafted approaches are difficult to execute due to the dynamic nature of ECG data, making machine learning solutions appealing for competent arrhythmia monitoring.
p-value: p=<0.0001
A novel machine learning approach using dipper throated optimization and KNN achieved 99.8% accuracy in classifying cardiac arrhythmias from ECG data.
Requires prospective validation before clinical use; leaves open generalizability of this ML approach for arrhythmia detection.
Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors (KNN) classifier. The proposed method makes advantage of the UCI repository, which has a 279-attribute high-dimensional cardiac arrhythmia dataset. The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features. The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients. This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature. The achieved classification accuracy using the proposed approach is 99.8%.
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Khafaga et al. (2022) studied Cardiac Arrhythmia (n=452). Dipper throated optimization algorithm with K-nearest neighbors (KNN) classifier vs. Other machine learning algorithms was evaluated on Classification accuracy (p=<0.0001). The dipper throated optimization algorithm combined with a K-nearest neighbors classifier achieved 99.8% accuracy in classifying cardiac arrhythmias from electrocardiogram data.
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