Autonomous Detection of Cardiac Ailments Using ECG Signals ‎and Random Forest Classifier

  • Authors

    • Gunturu Naga Lakshmi Af Research Scholars, Department of Computer Science and Engineering, Acharya Nagarjuna University College of Engineering and ‎Technology, Acharya Nagarjuna University
    • S. Nagakishore Bhavanam Professor, Department of Computer Science and Engineering, Mangalayatan University Jabalpur, Jabalpur
    • Vasujadevi Midasala Associate Professor, Department of Computer Science and Engineering, Mangalayatan University Jabalpur, Jabalpur
    • Dvsrk Chaitanya Department of Civil Engineering, Acharya Nagarjuna University College of Engineering and Technology, ‎Acharya Nagarjuna University
    https://doi.org/10.14419/hcmee071

    Received date: June 24, 2025

    Accepted date: October 24, 2025

    Published date: December 26, 2025

  • ECG; Anamoly Detection; Classification; Random Forest

  • 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.

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  • How to Cite

    Lakshmi, G. N. ., Bhavanam , S. N. ., Midasala , V. ., & Chaitanya , D. . (2025). Autonomous Detection of Cardiac Ailments Using ECG Signals ‎and Random Forest Classifier. International Journal of Basic and Applied Sciences, 14(8), 540-550. https://doi.org/10.14419/hcmee071