Predictive Modeling of Cardiac Arrhythmias Using Hybrid Feature Selection and Ensemble Learning Techniques

Narendra Kumar

Predictive Modeling of Cardiac Arrhythmias Using Hybrid Feature Selection and Ensemble Learning Techniques

Authors: Narendra Kumar

Published: 2025-04-28 19:27:54.122

Abstract

Cardiac arrhythmias pose a significant threat to global health, necessitating accurate and timely diagnosis for effective treatment. This research investigates the application of hybrid feature selection techniques combined with ensemble learning methods to improve the predictive accuracy of cardiac arrhythmia classification. We propose a novel approach that integrates filter-based (Information Gain) and wrapper-based (Genetic Algorithm) feature selection to identify the most relevant electrocardiogram (ECG) features. These selected features are then utilized to train various ensemble models, including Random Forest, Gradient Boosting Machines (GBM), and XGBoost. The performance of these models is evaluated using a comprehensive dataset of ECG recordings, and the results demonstrate a significant improvement in classification accuracy, precision, recall, and F1-score compared to traditional machine learning approaches. The proposed methodology offers a robust and efficient solution for cardiac arrhythmia prediction, potentially aiding clinicians in early diagnosis and personalized treatment planning.

Keywords

Cardiac Arrhythmia, Machine Learning, Feature Selection, Ensemble Learning, Predictive Modeling, Hybrid Algorithms, Medical Diagnosis, Data Mining, Healthcare AI, Random Forest.

References

Osowski, S., Hoai, L. T., & Markiewicz, T. (2004). Support vector machine-based expert system for reliable heartbeat recognition. IEEE Transactions on Biomedical Engineering, 51(4), 582-589.
de Chazal, P., Reilly, R. B., & Nolan, P. (2004). Automatic classification of heartbeats using ECG morphology and wavelet transform features. IEEE Transactions on Biomedical Engineering, 51(7), 1196-1206.
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adam, M., Gertych, A., ... & Tan, R. S. (2017). A deep convolutional neural network model for automated diagnosis of coronary artery disease. Journal of Medical Systems, 41(3), 21.
Nazari, N., Ayatollahi, A., & Setarehdan, S. K. (2016). ECG arrhythmia classification using combination of genetic algorithm and support vector machine. Computers in Biology and Medicine, 76, 139-146.
Gupta, C. N., Panigrahi, B. K., & Behera, H. S. (2018). Feature selection using genetic algorithm for ECG beat classification. Biomedical Signal Processing and Control, 40, 144-156.
Khan, A. M., Khan, M. A., Hussain, A., & Rehman, A. U. (2020). Hybrid feature selection for cardiac arrhythmia classification. IEEE Access, 8, 112684-112694.
Li, X., Zhou, X., Song, J., & Chen, D. (2021). Ventricular arrhythmia detection based on random forest. Computers in Biology and Medicine, 133, 104387.
Hasan, M. K., Islam, M. M., Khan, M. H., & Mamun, Q. (2022). Hybrid deep learning and ensemble learning approach for cardiac arrhythmia classification. Biomedical Signal Processing and Control, 71, 103142.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135-1144.
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189-1232.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 785-794.
Goldberger, J. J., Buxton, A. E., Cain, M. E., Estes, N. A. M., III, Garan, H., McAnulty, J. H., ... & Zipes, D. P. (2008). ACC/AHA/HRS 2008 guidelines for device-based therapy of cardiac rhythm abnormalities: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Revise the ACC/AHA/NASPE 2002 Guideline Update for Implantation of Cardiac Pacemakers and Antiarrhythmia Devices): developed in collaboration with the Heart Rhythm Society. Circulation, 117(21), e350-e408.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Artificial intelligence in healthcare. Nature Reviews Clinical Oncology, 16(1), 31-42.

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How to Cite

Narendra Kumar, (2025-04-28 19:27:54.122). Predictive Modeling of Cardiac Arrhythmias Using Hybrid Feature Selection and Ensemble Learning Techniques. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 2.

ISSN: 3048-6815

JANOLI International Journal of
Artificial Intelligence and its Applications ( JIJAIA )