Abstract
An arrhythmia is a problem with the rate or rhythm of the heartbeat. Variation in the heartbeat is measured by the time interval between the consecutive heartbeats known as HRV. If the arrhythmia is too slow or too fast, then it is identified as an irregular rhythm. This condition sometimes originates the tachycardia. The early detection and diagnoses of this disease make it consistent and effective to choose drugs related to arrhythmia. In this paper, the oversampling methods are used for cardiac arrhythmia arterial fibrillation such as SMOTE for nominal, random minority oversampling with replacement, adaptive synthetic sampling approach for imbalanced learning oversampling. The five machine learning classifiers are used that includes NB, SVM, DT, KNN, and LR. These oversampling methods are used to set the imbalanced data. Electrocardiogram (ECG) is one of the best ways to identify the heart rate, stroke, and heart disease by their electrical signals (electrodes, leads). The proposed methodology improves the performance in terms of best results that shows average accuracy for 87.04% achieved using the random minority oversampling with replacement. By using SMOTE method for nominal data, the accuracy achieved is 93.45%. Accuracy achieved using ADASYN method is 94.1% for multiple classifiers.
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Aggarwal, R., Thakral, P. (2022). Meticulous Presaging Arrhythmia Fibrillation for Heart Disease Classification Using Oversampling Method for Multiple Classifiers Based on Machine Learning. In: Verma, P., Charan, C., Fernando, X., Ganesan, S. (eds) Advances in Data Computing, Communication and Security. Lecture Notes on Data Engineering and Communications Technologies, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-16-8403-6_9
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