A Survey of Artificial Neural Networks and Their Applications in Prediction of Cardiac Arrhythmia Via Optimization Models and Metaheuristic Algorithms

Authors

  • Arman Daliri * Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran. https://orcid.org/0000-0002-0398-3052
  • Seyed Shervin Hosseini Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
  • Hesamaldin Edrisi Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
  • Pedram Khalaj Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
  • Parastoo Ghazanfari Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.
  • Avin Zarrabi Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran.

https://doi.org/10.48313/maa.v2i1.30

Abstract

Deep learning is one of the most well-known machine learning methods in various sciences and industries worldwide. With the passage of time and the increase of data by humans, analyzing this data, including numbers, images, signals, and sounds, has increased the importance of Artificial Neural Networks (ANNs). However, with a detailed understanding of  ANNs , one can think about the strengths and weaknesses of this widely used field in artificial intelligence. Several deep learning methods based on ANNs have been introduced and explained in this research. Then, cardiac arrhythmia has been discussed. A dataset of cardiac arrhythmia disease has been introduced to build a bridge between cardiac arrhythmia disease and the field of artificial intelligence and computers. In addition, the submitted dataset has been expertly examined, and all the features and types of cardiac rhythms present in it have been explained. Furthermore, metaheuristic optimization algorithms have been employed to improve the accuracy and performance of deep learning models in cardiac arrhythmia detection. These optimization techniques help to fine-tune model parameters efficiently and enhance diagnostic reliability. Finally, the conclusion summarizes and explains the research path and future work in this field.

Keywords:

Deep learning, Metaheuristic, Machine learning, Cardiac arrhythmia, Optimization

References

  1. [1] Goel, A., Goel, A. K., & Kumar, A. (2023). The role of artificial neural network and machine learning in utilizing spatial information. Spatial information research, 31(3), 275–285. https://doi.org/10.1007/s41324-022-00494-x%0A%0A
  2. [2] Daliri, A., Alimoradi, M., Zabihimayvan, M., & Sadeghi, R. (2024). World hyper-heuristic: A novel reinforcement learning approach for dynamic exploration and exploitation. Expert systems with applications, 244, 122931. https://doi.org/10.1016/j.eswa.2023.122931
  3. [3] Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x%0A%0A
  4. [4] Alimoradi, M., Zabihimayvan, M., Daliri, A., Sledzik, R., & Sadeghi, R. (2022). Deep neural classification of darknet traffic. In Artificial intelligence research and development (105–114). IOS press. https://doi.org/10.3233/FAIA220323
  5. [5] Daliri, A., Asghari, A., Azgomi, H., & Alimoradi, M. (2022). The water optimization algorithm: A novel metaheuristic for solving optimization problems. Applied intelligence, 52(15), 17990–18029. https://doi.org/10.1007/s10489-022-03397-4%0A%0A
  6. [6] Amiri, Z., Heidari, A., Navimipour, N. J., Unal, M., & Mousavi, A. (2024). Adventures in data analysis: A systematic review of Deep Learning techniques for pattern recognition in cyber-physical-social systems. Multimedia tools and applications, 83(8), 22909–22973. https://doi.org/10.1007/s11042-023-16382-x%0A%0A
  7. [7] Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., & Aljaaf, A. J. (2020). A systematic review on supervised and unsupervised machine learning algorithms for data science. Supervised and unsupervised learning for data science, 3–21. https://doi.org/10.1007/978-3-030-22475-2_1%0A%0A
  8. [8] Alimoradi, M., Azgomi, H., & Asghari, A. (2022). Trees social relations optimization algorithm: A new swarm-based metaheuristic technique to solve continuous and discrete optimization problems. Mathematics and computers in simulation, 194, 629–664. https://doi.org/10.1016/j.matcom.2021.12.010
  9. [9] Daliri, A., Khalilian, M., Mohammadzadeh, J., & Hosseini, S. S. (2025). Optimized Active fuzzy deep federated learning for predicting autism spectrum disorder. Network modeling analysis in health informatics and bioinformatics, 14(1), 31. https://doi.org/10.1007/s13721-025-00523-3%0A%0A
  10. [10] Daliri, A., Branch, K., Sheikha, M., Roudposhti, K. K., Branch, L., Alimoradi, M., Mohammadzadeh, J. (2025). Optimized categorical boosting for gastric cancer classification using heptagonal reinforcement learning and the water optimization algorithm. Lecture notes in computer science. Springer. https://www.researchgate.net/profile/Arman-Daliri/publication/389465487
  11. [11] Mahdavi, N., Daliri, A., Zabihimayvan, M., Yaghooti, Y., Mir, M. M., Ghazanfari, P., Sadeghi, R. (2025). WHFDL: An explainable method based on world hyper-heuristic and fuzzy deep learning approaches for gastric cancer detection using metabolomics data. BioData mining, 18(1), 72. https://doi.org/10.1186/s13040-025-00486-1%0A%0A
  12. [12] Boulesteix, A.-L., & Schmid, M. (2014). Machine learning versus statistical modeling. Biometrical journal, 56(4), 588–593. https://doi.org/10.1002/bimj.201300226
  13. [13] Chellappa, R., Theodoridis, S., & Van Schaik, A. (2021). Advances in machine learning and deep neural networks. Proceedings of the ieee, 109(5), 607–611. https://doi.org/10.1109/JPROC.2021.3072172
  14. [14] Biau, G., & Scornet, E. (2016). A random forest guided tour. Test, 25(2), 197–227. https://doi.org/10.1007/s11749-016-0481-7%0A%0A
  15. [15] Loh, W. Y. (2006). Logistic regression tree analysis. In Springer handbook of engineering statistics (537–549). Springer. https://doi.org/10.1007/978-1-84628-288-1_29
  16. [16] Freund, Y., & Mason, L. (1999). The alternating decision tree learning algorithm [International Conference on Machine Learning (ICML)]. Icml (Vol. 99, pp. 124–133). https://staff.icar.cnr.it/manco/Teaching/2006/datamining/articoli/Freund_Atrees.pdf
  17. [17] Kumar, M., & Yadav, N. (2011). Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: A survey. Computers mathematics with applications, 62(10), 3796–3811. https://doi.org/10.1016/j.camwa.2011.09.028
  18. [18] Krichen, M. (2023). Convolutional neural networks: A survey. Computers, 12(8), 151. https://doi.org/10.3390/computers12080151
  19. [19] Williams, R. J., & Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2), 270–280. https://doi.org/10.1162/neco.1989.1.2.270
  20. [20] Chiroma, H., Abdullahi, U. A., Abdulhamid, S. M., Alarood, A. A., Gabralla, L. A., Rana, N. (2018). Progress on artificial neural networks for big data analytics: a survey. IEEE access, 7, 70535–70551. https://doi.org/10.1109/ACCESS.2018.2880694
  21. [21] Langenberg, C., Hingorani, A. D., & Whitty, C. J. M. (2023). Biological and functional multimorbidity from mechanisms to management. Nature medicine, 29(7), 1649–1657. https://doi.org/10.1038/s41591-023-02420-6%0A%0A
  22. [22] Bhatt, C. M., Patel, P., Ghetia, T., & Mazzeo, P. L. (2023). Effective heart disease prediction using machine learning techniques. Algorithms, 16(2), 88. https://doi.org/10.3390/a16020088
  23. [23] Luengo-Fernandez, R., Little, M., Gray, A., Torbica, A., Maggioni, A. P., Huculeci, R., Leal, J. (2024). Cardiovascular disease burden due to productivity losses in european society of cardiology countries. European heart journal-quality of care and clinical outcomes, 10(1), 36–44. https://doi.org/10.1093/ehjqcco/qcad031
  24. [24] You, W., Feng, S., & Donnelly, F. (2023). Total meat (Flesh) supply may be a significant risk factor for cardiovascular diseases worldwide. Food science & nutrition, 11(6), 3203–3212. https://doi.org/10.1002/fsn3.3300
  25. [25] Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information fusion, 96, 156–191. https://doi.org/10.1016/j.inffus.2023.03.008
  26. [26] Sawhney, R., Malik, A., Sharma, S., & Narayan, V. (2023). A comparative assessment of artificial intelligence models used for early prediction and evaluation of chronic kidney disease. Decision analytics journal, 6, 100169. https://doi.org/10.1016/j.dajour.2023.100169
  27. [27] Tang, L., Yang, J., Wang, Y., & Deng, R. (2023). Recent advances in cardiovascular disease biosensors and monitoring technologies. ACS sensors, 8(3), 956–973. https://doi.org/10.1021/acssensors.2c02311
  28. [28] Alimoradi, M., Sadeghi, R., Daliri, A., & Zabihimayvan, M. (2025). Statistic deviation mode balancer (SDMB): A novel sampling algorithm for imbalanced data. Neurocomputing, 624, 129484. https://doi.org/10.1016/j.neucom.2025.129484
  29. [29] Nguyen, T., Wei, Y., Nakada, Y., Chen, J. Y., Zhou, Y., Walcott, G., & Zhang, J. (2023). Analysis of cardiac single-cell RNA-sequencing data can be improved by the use of artificial-intelligence-based tools. Scientific reports, 13(1), 6821. https://doi.org/10.1038/s41598-023-32293-1%0A%0A
  30. [30] Subramani, S., Varshney, N., Anand, M. V., Soudagar, M. E. M., Al-Keridis, L. A., Upadhyay, T. K. (2023). Cardiovascular diseases prediction by machine learning incorporation with deep learning. Frontiers in medicine, 10, 1150933. https://doi.org/10.3389/fmed.2023.1150933
  31. [31] Azevedo, F. A. C., Carvalho, L. R. B., Grinberg, L. T., Farfel, J. M., Ferretti, R. E. L., Leite, R. E. P., Herculano-Houzel, S. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of comparative neurology, 513(5), 532–541. https://doi.org/10.1002/cne.21974
  32. [32] Admon, M. R., Senu, N., Ahmadian, A., Abdul Majid, Z., & Salahshour, S. (2023). A new efficient algorithm based on feedforward neural network for solving differential equations of fractional order. Communications in nonlinear science and numerical simulation, 117, 106968. https://doi.org/10.1016/j.cnsns.2022.106968
  33. [33] De Schepper, R., Geminiani, A., Masoli, S., Rizza, M. F., Antonietti, A., Casellato, C., D’Angelo, E. (2022). Model simulations unveil the structure-function-dynamics relationship of the cerebellar cortical microcircuit. Communications biology, 5(1), 1240. https://doi.org/10.1038/s42003-022-04213-y%0A%0A
  34. [34] Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J. (2023). A survey of uncertainty in deep neural networks. Artificial intelligence review, 56(1), 1513–1589. https://doi.org/10.1007/s10462-023-10562-9%0A%0A
  35. [35] Daliri, A., Khoshbakhti, M., Samadi, M. K., Rahiminia, M., Zabihimayvan, M., & Sadeghi, R. (2024). Equilateral Active Learning (EAL): A novel framework for predicting autism spectrum disorder based on active fuzzy federated learning. Artificial intelligence and social computing, 122, 182–192. https://doi.org/10.54941/ahfe1004655
  36. [36] Mohammadifar, A., Samadbin, H., & Daliri, A. (2023). Accurate autism spectrum disorder prediction using support vector classifier based on federated learning (svcfl). ArXiv preprint arxiv:2311.04606. https://doi.org/10.48550/arXiv.2311.04606
  37. [37] Daliri, A., Zabihimayvan, M., & Saleh, K. (2024). Vector Result Rate (VRR): A novel method for fraud detection in mobile payment systems. Artificial intelligence and social computing, 122(122), 52_61. https://doi.org/10.54941/ahfe1004641
  38. [38] Samadbin, H., & Daliri, A. (2023). Right choice of classification algorithms based on reinforcement learning for prediction of non-alcoholic fatty liver. The first national conference on research and innovation in artificial intelligence (p. 16_17). Islamic Azad University, Karaj Branch. https://www.researchgate.net/profile/Arman-Daliri/publication/384426798
  39. [39] Ghosh, J., & Nag, A. (2001). An overview of radial basis function networks. Radial basis function networks 2: new advances in design, 1–36. https://doi.org/10.1007/978-3-7908-1826-0_1%0A%0A
  40. [40] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems, 28. https://proceedings.neurips.cc/paper_files/paper/2015/hash/86df7dcf
  41. [41] Ranjana, P., & others. (2023). Fuzzy logic based deep learning approach (FRNN) for autism spectrum disorder detection. 2023 ieee international conference on integrated circuits and communication systems (icicacs) (pp. 1–5). IEEE. https://doi.org/10.1109/ICICACS57338.2023.10099529
  42. [42] Lakhan, A., Mohammed, M. A., Abdulkareem, K. H., Hamouda, H., & Alyahya, S. (2023). Autism spectrum disorder detection framework for children based on federated learning integrated CNN-LSTM. Computers in biology and medicine, 166, 107539. https://doi.org/10.1016/j.compbiomed.2023.107539
  43. [43] Dey, R., & Salem, F. M. (2017). Gate-variants of gated recurrent unit (GRU) neural networks. 2017 ieee 60th international midwest symposium on circuits and systems (mwscas) (pp. 1597–1600). IEEE. https://doi.org/10.1109/MWSCAS.2017.8053243
  44. [44] Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design, and application. Journal of microbiological methods, 43(1), 3–31. https://doi.org/10.1016/S0167-7012(00)00201-3
  45. [45] Lamamra, K., Belarbi, K., & Boukhtini, S. (2014). Box and Jenkins nonlinear system modelling using RBF neural networks designed by NSGAII. In Computational intelligence applications in modeling and control (pp. 229–254). Springer. https://doi.org/10.1007/978-3-319-11017-2_10%0A%0A
  46. [46] Jafarian, A., Measoomy Nia, S., Khalili Golmankhaneh, A., & Baleanu, D. (2018). On artificial neural networks approach with new cost functions. Applied mathematics and computation, 339, 546–555. https://doi.org/10.1016/j.amc.2018.07.053
  47. [47] Lange, S., & Riedmiller, M. (2010). Deep auto-encoder neural networks in reinforcement learning. The 2010 international joint conference on neural networks (ijcnn) (1–8). IEEE. https://doi.org/10.1109/IJCNN.2010.5596468
  48. [48] Papadopoulos, D., & Karalis, V. D. (2023). Variational autoencoders for data augmentation in clinical studies. Applied sciences, 13(15), 8793. https://doi.org/10.3390/app13158793
  49. [49] El Bourakadi, D., Ramadan, H., Yahyaouy, A., & Boumhidi, J. (2023). A robust energy management approach in two-steps ahead using deep learning BiLSTM prediction model and type-2 fuzzy decision-making controller. Fuzzy optimization and decision making, 22(4), 645–667. https://doi.org/10.1007/s10700-022-09406-y%0A%0A
  50. [50] Zheng, J., Zhang, J., Danioko, S., Yao, H., Guo, H., & Rakovski, C. (2020). A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Scientific data, 7(1), 48. https://doi.org/10.1038/s41597-020-0386-x%0A%0A
  51. [51] Merdjanovska, E., & Rashkovska, A. (2023). A framework for comparative study of databases and computational methods for arrhythmia detection from single-lead ECG. Scientific reports, 13(1), 11682. https://doi.org/10.1038/s41598-023-38532-9%0A%0A
  52. [52] Yoon, T., & Kang, D. (2023). Bimodal CNN for cardiovascular disease classification by co-training ECG grayscale images and scalograms. Scientific reports, 13(1), 2937. https://doi.org/10.1038/s41598-023-30208-8%0A%0A
  53. [53] Wang, N., Feng, P., Ge, Z., Zhou, Y., Zhou, B., & Wang, Z. (2023). Adversarial spatiotemporal contrastive learning for electrocardiogram signals. IEEE transactions on neural networks and learning systems, 35(10), 13845–13859. https://doi.org/10.1109/TNNLS.2023.3272153
  54. [54] Le, D., Truong, S., Brijesh, P., Adjeroh, D. A., & Le, N. (2023). sCL-ST: Supervised contrastive learning with semantic transformations for multiple lead ECG arrhythmia classification. IEEE journal of biomedical and health informatics, 27(6), 2818–2828. https://doi.org/10.1109/JBHI.2023.3246241
  55. [55] Le, K. H., Pham, H. H., Nguyen, T. B. T., Nguyen, T. A., Thanh, T. N., & Do, C. D. (2023). Lightx3ecg: A lightweight and explainable deep learning system for 3-lead electrocardiogram classification. Biomedical signal processing and control, 85, 104963. https://doi.org/10.1016/j.bspc.2023.104963
  56. [56] Yildirim, O., Talo, M., Ciaccio, E. J., San Tan, R., & Acharya, U. R. (2020). Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records. Computer methods and programs in biomedicine, 197, 105740. https://doi.org/10.1016/j.cmpb.2020.105740
  57. [57] Sadad, T., Safran, M., Khan, I., Alfarhood, S., Khan, R., & Ashraf, I. (2023). Efficient classification of ECG images using a lightweight CNN with attention module and IoT. Sensors, 23(18), 7697. https://doi.org/10.3390/s23187697
  58. [58] Ozpolat, Z., & Karabatak, M. (2023). Performance evaluation of quantum-based machine learning algorithms for cardiac arrhythmia classification. Diagnostics, 13(6), 1099. https://doi.org/10.3390/diagnostics13061099
  59. [59] Hu, L., Cai, W., Chen, Z., & Wang, M. (2024). A lightweight U-Net model for denoising and noise localization of ECG signals. Biomedical signal processing and control, 88, 105504. https://doi.org/10.1016/j.bspc.2023.105504
  60. [60] Hu, R., Chen, J., & Zhou, L. (2023). Spatiotemporal self-supervised representation learning from multi-lead ECG signals. Biomedical signal processing and control, 84, 104772. https://doi.org/10.1016/j.bspc.2023.104772
  61. [61] Berger, L., Haberbusch, M., & Moscato, F. (2023). Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges. Artificial intelligence in medicine, 143, 102632. https://doi.org/10.1016/j.artmed.2023.102632
  62. [62] Mehari, T., & Strodthoff, N. (2023). Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata. IEEE journal of biomedical and health informatics, 27(11), 5326–5334. https://doi.org/10.1109/JBHI.2023.3310989
  63. [63] Daliri, A., Sadeghi, R., Sedighian, N., Karimi, A., & Mohammadzadeh, J. (2024). Heptagonal Reinforcement Learning (HRL): A novel algorithm for early prevention of non-sinus cardiac arrhythmia. Journal of ambient intelligence and humanized computing, 15(4), 2601–2620. https://doi.org/10.1007/s12652-024-04776-0%0A%0A
  64. [64] McConnell, B., Del Monaco, D., Zabihimayvan, M., Abdollahzadeh, F., & Hamada, S. (2023). Phishing attack detection: an improved performance through ensemble learning. International conference on artificial intelligence and soft computing (pp. 145–157). https://doi.org/10.1007/978-3-031-42508-0_14%0A%0A
  65. [65] Awal, M. A., Mostafa, S. S., Ahmad, M., Alahe, M. A., Rashid, M. A., Kouzani, A. Z., & Mahmud, M. A. P. (2021). Design and optimization of ECG modeling for generating different cardiac dysrhythmias. Sensors, 21(5), 1638. https://doi.org/10.3390/s21051638
  66. [66] Mohebbanaaz, M., Sai, Y. P., & Kumari, L. V. R. (2021). Detection of cardiac arrhythmia using deep CNN and optimized SVM. Indonesian journal of electrical engineering and computer science, 24(1), 217–225. https://doi.org/10.11591/ijeecs.v24.i1.pp217-225
  67. [67] Li, L., Ren, W., Lei, Y., Xu, L., & Ning, X. (2025). Hybrid CNN-transformer-WOA model with XGBoost-SHAP feature selection for arrhythmia risk prediction in acute myocardial infarction patients. BMC medical informatics and decision making, 25(1), 291. https://doi.org/10.1186/s12911-025-03127-z%0A%0A
  68. [68] Jekova, I., & Krasteva, V. (2021). Optimization of end-to-end convolutional neural networks for analysis of out-of-hospital cardiac arrest rhythms during cardiopulmonary resuscitation. Sensors, 21(12), 4105. https://doi.org/10.3390/s21124105

Downloads

Published

2025-03-21

Issue

Vol. 2 No. 1 (2025): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Daliri, A., Hosseini, S. S. ., Edrisi, H., Khalaj, P. ., Ghazanfari, P. ., & Zarrabi, A. . (2025). A Survey of Artificial Neural Networks and Their Applications in Prediction of Cardiac Arrhythmia Via Optimization Models and Metaheuristic Algorithms. Metaheuristic Algorithms With Applications, 2(1), 82-98. https://doi.org/10.48313/maa.v2i1.30

Similar Articles

You may also start an advanced similarity search for this article.