Autonomous Detection of Cardiac Ailments Using ECG Signals and Random Forest Classifier
Year: 2025
Type: article
Abstract: The ECG signal is a vital resource in diagnosing various cardiac ailments, providing crucial information for accurate decision-making re-garding different types of heart diseases. To achieve autonomous detection of cardiac ailments, several strategies have been proposed to extract critical features from the ECG signal with utmost precision. In this study, a state-of-the-art methodology is introduced for the auto-matic detection of cardiac ailments. The proposed methodology encompasses three main steps: pre-processing, feature extraction, and classi-fication. In the pre-processing step, a Butterworth third-order band-pass filter is utilized to refine the ECG signal. For feature extraction, a four-level maximal overlap discrete wavelet packet transform (MODWPT) technique is employed, utilizing the symlet wavelet as the mother wavelet. In the final stage of classification, five supervised machine learning algor2 ns are applied to classify the considered three cardiac ailments from the MIT-BIH database: Arrhythmia, Congestive Heart Failure, Atrial Fibrillation, and Normal Sinus Rhythm. The algorithms used include Support Vector Machine (SVM), K-nearest Neighbour (KNN). Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF). These classifiers yield overall accuracies of 90.83%, 90.56%, 90.28%, 91.39%, and 91.94% respectively. Based on the results, it is evident that the Random Forest classifier exhibits superior accuracy among the proposed methodology's classifiers for the multiclass classi-fication of cardiac ailments.
DOI: 10.14419/hcmee071
Institutions: Acharya Nagarjuna University, Mangalayatan University
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7117359653
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