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A Hybrid Optimized Framework for the Classification and Detection of Cardiac Diseases
Subject area: Science,Engineering and Technology · Area of research: HealthCare
DOI: https://doi.org/10.64388/IREV10I2-1722160
Abstract
The research study ventures into heart signal evaluation and diagnosis machine learning using Vision Transformer (ViT) as the main computation building block. In his work Vazifecas constructs a one- dimensional signal for separating P, QRS, and T segments of heart electrical waves from ECG signals. Staking Ensemble enhances The Cardiovascular ECG Images dataset robustness levels. A Whale- Optimization performs model diagnosis of heart disease in the optimization process of heart signals.
Keywords
Cardiovascular Disease, Convolutional Neural Network (CNN), vision transformer (VIT), Multi-Layer Perceptron (MLP), Whole Optimization Algorithm (WOA).
References
[1] Wasimuddin, M., et al. (2020). Stages-based ECG signal analysis: A survey. IEEE Access, 8, 177782–177803. https://doi.org/10.1109/ACCESS.2020.3026968
[2] Bhattarai, S. P., et al. (2025). Estimating very low ejection fraction from 12-lead ECG. Journal of Electrocardiology, 89. ISSN 0022-0736.
[3] Hasan, M. N., et al. (2025). Ensemble-based deep learning for cardiovascular prediction from ECG. Engineering Applications of Artificial Intelligence, 141. ISSN 0952-1976.
[4] Bouqentar, M. A., et al. (2024). Early heart disease prediction using feature engineering & machine learning. Heliyon, 10(19). ISSN 2405-8440.
[5] Karthik, et al. (2022). Automated deep learning for cardiovascular diagnosis using ECG. Computer Systems Science and Engineering, 42(1). https://doi.org/10.32604/csse.2022.021698
[6] Kilimci, Z. H., et al. (2023). Heart disease detection using Vision Transformers from ECG (arXiv preprint). https://doi.org/10.48550/arXiv.2310.12630
[7] Mahmoud, S., et al. (2022). Cardiovascular disease prediction using modified ResNet-50. In Proceedings of the International Conference on Computer Theory and Applications (ICCTA) (pp. 18–24).
[8] Adnan, J., Nik Daud, N. G., Ahmad, S., Mat, M. H., Ishak, M. T., & F. (n.d.). Heart abnormality activity detection using multilayer perceptron (MLP) network. (Incomplete reference as provided.)
[9] Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.
[10] Zhang, J., et al. (2019). BMI and mortality in heart failure patients: A meta-analysis. Clinical Research in Cardiology, 108, 119–132.
[11] Mahmoud, S., et al. (2020). ECG-based cardiovascular disease detection using modified ResNet-50. In Proceedings of the International Conference on Computer Engineering Systems (ICCES) (pp. 1–6). https://doi.org/10.1109/ICCES51560.2020.9334656
How to cite this paper
@article{1722160,
author = {Pothanaboina Lavanya, Chandana Padarthi, Sudagani Chandra Bhavani , Tokala Sravani, Dr. R. Sudha Kishore; Dr. K. Kranthi Kumar},
title = {A Hybrid Optimized Framework for the Classification and Detection of Cardiac Diseases},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {209-217},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722160.pdf},
abstract = {The research study ventures into heart signal evaluation and diagnosis machine learning using Vision Transformer (ViT) as the main computation building block. In his work Vazifecas constructs a one- dimensional signal for separating P, QRS, and T segments of heart electrical waves from ECG signals. Staking Ensemble enhances The Cardiovascular ECG Images dataset robustness levels. A Whale- Optimization performs model diagnosis of heart disease in the optimization process of heart signals.},
keywords = {Cardiovascular Disease, Convolutional Neural Network (CNN), vision transformer (VIT), Multi-Layer Perceptron (MLP), Whole Optimization Algorithm (WOA).},
month = {August},
doi = {https://doi.org/10.64388/IREV10I2-1722160}
}