A hybrid fine-tuned optimizer for enhancing ECG data security in heart attack detection systems
*Ajmeera KiranCorresponding authorkbgrguptha@gmail.comkiranphd.jntuh@gmail.comajmeerakiran@mlrinstitutions.ac.inAffiliation 1Muma College of Business8350 N. Tamiami Trail SarasotaUniversity of South FloridaFlorida, FL 33620, U.S.A.Affiliation 2Department of Computer Science and EngineeringMLR Institute of TechnologyHyderabad, Telangana, 500043, IndiaView full profile → , Bhuvan Unhelkarbunhelkar@usf.eduMuma College of Business8350 N. Tamiami Trail SarasotaUniversity of South FloridaFlorida, FL 34243, U.S.A.0000-0003-1118-3837View full profile → , S. Siva Shankardrsivashankars@gmail.comDepartment of Computer Science and EngineeringKG Reddy College of Engineering and TechnologyHyderabad, Telangana, 500075, India0000-0002-7616-6088View full profile → , Tulika Chakrabartitulika.chakrabarti@spsu.ac.inDepartment of ChemistrySir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, lndiaView full profile → , Prasun Chakrabartidrprasun.cse@gmail.comDepartment of Computer Science and EngineeringSir Padampat Singhania UniversityUdaipur, Rajasthan, 313601, lndia0000-0001-8062-4144View full profile → , B. Sivaneasansivaneasan@singaporetech.edu.sgSpecialist Adult Educator EngineeringElectrical Power Engineering ProgrammeSingapore Institute of Technology828608, SingaporeView full profile → , Martin MargalaMartin.margala@louisiana.eduSchool of Computing and InformaticsUniversity of Louisiana at LafayetteLA 70503, U.S.A.View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 13 Feb 2024
- Published Online:
- 18 Dec 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1808
- Pages:
- 2309–2323
Abstract
Keywords
Subject Classifications
References
[1] S. Mandala, S. S. Amini, A. R. Syaifullah, M. Pramudyo, S. Nurmaini, and A. H. Abdullah, “Enhanced myocardial infarction identification in phonocardiogram signals using segmented feature extraction and transfer learning-based classification,” IEEE Access, vol. 11, pp. 136654–136665 (2023).
[2] B. Ramesh and K. Lakshmanna, “A Novel Early Detection and Prevention of Coronary Heart Disease Framework Using Hybrid Deep Learning Model and Neural Fuzzy Inference System,” IEEE Access, vol. 12, pp. 26683–26695 (2024), doi: 10.1109/ACCESS.2024.3366537.
[3] S. Akter, F. M. J. M. Shamrat, S. Chakraborty, A. Karim, and S. Azam, “Covid-19 detection using deep learning algorithm on chest X-ray images,” Biology (Basel)., vol. 10, no. 11 (2021), doi: 10.3390/biology10111174.
[4] S. M. Rezaeijo, M. Ghorvei, R. Abedi-Firouzjah, H. Mojtahedi, and H. Entezari Zarch, “Detecting COVID-19 in chest images based on deep transfer learning and machine learning algorithms,” Egypt. J. Radiol. Nucl. Med., vol. 52, no. 1, Dec. (2021), doi: 10.1186/s43055-021-00524-y.
[5] V. Atanasoski, J. Petrović, L. P. Maneski, M. Miletić, M. Babić, A. Nikolić, and M. D. Ivanović, “A morphology-preserving algorithm for denoising of EMG-contaminated ECG signals,” IEEE Open Journal of Engineering in Medicine and Biology (2024).
[6] P. Madan, V. Singh, D. P. Singh, M. Diwakar, B. Pant, and A. Kishor, “A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification,” Bioengineering, vol. 9, no. 4 (2022), doi: 10.3390/bioengineering9040152.
[7] M. N. Hasan, M. A. Hossain, and A. S. Akash, “Detection of Cardiovascular Disease from ECG Image Using Optimized Weighted Average Ensemble Machine Learning Technique,” in 2023 26th International Conference on Computer and Information Technology, ICCIT 2023 (2023). doi: 10.1109/ICCIT60459.2023.10441119.
[8] T. Mahmood, A. Rehman, T. Saba, T. J. Alahmadi, M. Tufail, S. A. O. Bahaj, and Z. Ahmad, “Enhancing Coronary Artery Disease Prognosis: A Novel Dual-Class Boosted Decision Trees Strategy for Robust Optimization,” IEEE Access (2024).
[9] S. Muthumeena and L. P. On, “Deep Learning Framework for Cardio Vascular Disease Prediction Using ECG Images,” 2024 Int. Conf. Smart Syst. Electr. Electron. Commun. Comput. Eng., pp. 244–249 (2024), doi: 10.1109/ICSSEECC61126.2024.10649540.
[10] A. A. Aleidan, Q. Abbas, Y. Daadaa, I. Qureshi, G. Perumal, M. E. Ibrahim, and A. E. Ahmed, “Biometric-based human identification using ensemble-based technique and ECG signals,” Applied Sciences, vol. 13, no. 16, p. 9454 (2023).
[11] R. Al-Tam, A. Al-Hejri, E. Naji, … F. H.-I., and U. 2024, “A Hybrid Framework of Transformer Encoder and Residential Conventional for Cardiovascular Disease Recognition Using Heart Sounds,” IEEE Access (2024), doi: 10.1109/ACCESS.2024.3451660.
[12] R. J. Urbanowicz, M. Meeker, W. La Cava, R. S. Olson, and J. H. Moore, “Relief-based feature selection: Introduction and review,” Journal of Biomedical Informatics, vol. 85. pp. 189–203 (2018). doi: 10.1016/j.jbi.2018.07.014.
[13] N. K. Chauhan and N. Kaur, “Predicting Cardiovascular Diseases Using Machine Learning Integrated with Feature Selection Through Blue Whale Optimization,” in 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, Institute of Electrical and Electronics Engineers Inc., pp. 464–469 (2023). doi: 10.1109/UPCON59197.2023.10434597.
[14] S. P. Patro, N. Padhy, and R. D. Sah, “An improved ensemble learning approach for the prediction of cardiovascular disease using majority voting prediction,” Int. J. Model. Identif. Control, vol. 41, no. 1–2, pp. 68–86 (2022), doi: 10.1504/ijmic.2022.127098.
[15] A. Pramanik, P. Rajput, and S. Aluvala, “Applying Healthcare Analytics to Diagnose and Predict Coronary Artery Disease Using Machine Learning Techniques,” in Proceedings - 2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023, Institute of Electrical and Electronics Engineers Inc., pp. 610–614 (2023). doi: 10.1109/ICACCTech61146.2023.00104.
[16] A. Raj, A. Kumar, S. Kumar, and S. Budhiraja, “Heart Disease Prediction using Machine Learning,” in 2023 9th International Conference on Signal Processing and Communication, ICSC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 113–119 (2023). doi: 10.1109/ICSC60394.2023.10441292.
[17] A. K. Berdaly and Z. M. Abdiahmetova, “PREDICTING HEART DISEASE USING MACHINE LEARNING ALGORITHMS,” Kazn. Bull. Math. Mech. Comput. Sci. Ser., vol. 115, no. 3, pp. 101–111 (2022), doi: 10.26577/JMMCS.2022.v115.i3.10.
[18] S. N. Ajani, P. Khobragade, M. Dhone, B. Ganguly, N. Shelke, and N. Parati, “Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing,” Int. J. Intell. Syst. Appl. Eng., vol. 12, no. 7s, pp. 546–559 (2024).
[19] E. Irmak, “COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model,” Phys. Eng. Sci. Med., vol. 45, no. 1, pp. 167–179, Mar. (2022), doi: 10.1007/s13246-022-01102-w.
[20] T. Anwar and S. Zakir, “Effect of Image Augmentation on ECG Image Classification using Deep Learning,” in 2021 International Conference on Artificial Intelligence, ICAI 2021, 2021, pp. 182–186. doi: 10.1109/ICAI52203.2021.9445258.




