Publication Date: 2021/12/08
Abstract: Arrhythmias also known as dysrhythmia is a heart ailment that arises when electrical signals that coordinate the heartbeats do not work appropriately, they are often precursors to a number of heart diseases which may be terminal, and early detection and adequate treatment can save life, in this paper we propose a classification technique that blends two good performing machine learning algorithms to enhance the accuracy of detecting arrhythmia using Electrocardiogram (ECG) data and Weka machine learning tool, these algorithms include the J.48 and Random Forest algorithms combined with an ensemble algorithm called Stacking; For this experiment the MIT-BIH ECG dataset from Kaggle.com was used to train, test and validate the hybrid algorithm. This dataset used classified ECG data into the 5 super class of arrhythmia approved by the association for the advancement of medical instrumentation (AAMI) to be detectable by equipment and methods, they include normal sinus (N), fusion beat (F), supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), and unknown beat (Q). the hybrid algorithm “stacked random forest and j.48) outperformed the other individual algorithms, the performance metrics gotten include 97.63% accuracy, an approximate sensitivity (recall) and Positive predictivity (precision) value of 0.98, other metrics includes a weighted precision recall curve area of 0.97, receiver operator characteristics area of 0.96 and test time of 1.66 seconds and finally a model size of 38.2mb which is suitable for building application for mobile devices.
Keywords: Machine Learning, Arrhythmia Classification, ECG, Random Forest, J.48, Stacking Ensemble.
DOI: No DOI Available
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT21NOV273.pdf
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