Worldwide, some 17.9 million survives are lost each year due to heart disease (HD), which is acknowledged by the World Health Organisation (WHO) as top cause of mortality. In order to simplify further action, HD prediction—a difficult problem—can give a computerised estimate of the HD level. Improving patient outcomes and allowing for timely medical interventions are both made possible by early detection and accurate calculation of HD. As a result, HD prediction has garnered a great deal of interest from healthcare facilities around the globe. There has been encouraging progress in the detection of cardiac illness thanks to recent developments in machine learning (ML). Transparency and explainability, in addition to generalisability and robustness, are crucial for ML models to be used in therapeutic settings. The efficient prediction and diagnosis of numerous diseases was greatly aided by systems based on Deep Learning (DL). By combining Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTMs), besides Attention Mechanisms (CNN-AM), this paper aims to build a strong HD prediction scheme. Minimal preparation is necessary for this procedure. To extract spatial features, CNN is used. To extract temporal characteristics, Bi-LSTM is used. Lastly, to filter out the outcomes of the more to ighted channel output classification, two channel to ights are allotted through the attention mechanism. The proposed model's parameters are fine-tuned using a new optimisation approach known as Newton-Raphson-based Optimiser (NRO), which ultimately leads to better classification accuracy. With accuracy of 95.3% on the Cleveland dataset and 98.1% on the Framingham dataset, respectively, the optimised CNN-BiLSTM-AM model demonstrated the best performance in the experimental findings.
Keywords
Heart Disease; Newton-Raphson-Based Optimizer; Bidirectional Long Short-Term Memory; Attention Mechanism; Cleveland and Framingham Datasets.
S. H. Bani Hani and M. M. Ahmad, “Machine-learning Algorithms for Ischemic Heart Disease Prediction:A Systematic Review,” Current Cardiology Reviews, vol. 19, no. 1, Jan. 2023, doi: 10.2174/1573403x18666220609123053.
S. Nandy, M. Adhikari, V. Balasubramanian, V. G. Menon, X. Li, and M. Zakarya, “An intelligent heart disease prediction system based on swarm-artificial neural network,” Neural Computing and Applications, vol. 35, no. 20, pp. 14723–14737, May 2021, doi: 10.1007/s00521-021-06124-1.
A. L. Yadav, K. Soni, and S. Khare, “Heart Diseases Prediction using Machine Learning,” 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), vol. 09, pp. 1–7, Jul. 2023, doi: 10.1109/icccnt56998.2023.10306469.
V. S. Anusuya Devi, A. Thirumalraj, B. P. Kavin, and G. H. Seng, “Securing the Predicted Disease Data using Transfer Learning in Cloud-Based Healthcare 5.0,” Intelligent Systems and Industrial Internet of Things for Sustainable Development, pp. 101–117, Mar. 2024, doi: 10.1201/9781032642789-5.
W. A. W. A. Bakar, N. L. N. B. Josdi, M. B. Man, and M. A. B. Zuhairi, “A Review: Heart Disease Prediction in Machine Learning & Deep Learning,” 2023 19th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), Mar. 2023, doi: 10.1109/cspa57446.2023.10087837.
P. Dileep et al., “An automatic heart disease prediction using cluster-based bi-directional LSTM (C-BiLSTM) algorithm,” Neural Computing and Applications, vol. 35, no. 10, pp. 7253–7266, Mar. 2022, doi: 10.1007/s00521-022-07064-0.
M. S. A. Reshan, S. Amin, M. A. Zeb, A. Sulaiman, H. Alshahrani, and A. Shaikh, “A Robust Heart Disease Prediction System Using Hybrid Deep Neural Networks,” IEEE Access, vol. 11, pp. 121574–121591, 2023, doi: 10.1109/access.2023.3328909.
Z. Noroozi, A. Orooji, and L. Erfannia, “Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction,” Scientific Reports, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-49962-w.
Md. I. Hossain et al., “Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparison,” Iran Journal of Computer Science, vol. 6, no. 4, pp. 397–417, Jun. 2023, doi: 10.1007/s42044-023-00148-7.
K. Arumugam, M. Naved, P. P. Shinde, O. Leiva-Chauca, A. Huaman-Osorio, and T. Gonzales-Yanac, “Multiple disease prediction using Machine learning algorithms,” Materials Today: Proceedings, vol. 80, pp. 3682–3685, 2023, doi: 10.1016/j.matpr.2021.07.361.
S. Mohammad Ganie, P. Kanti Dutta Pramanik, M. Bashir Malik, A. Nayyar, and K. Sup Kwak, “An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms,” Computer Systems Science and Engineering, vol. 46, no. 3, pp. 3993–4006, 2023, doi: 10.32604/csse.2023.035244.
G. A. Ansari, S. S. Bhat, M. D. Ansari, S. Ahmad, J. Nazeer, and A. E. M. Eljialy, “Performance Evaluation of Machine Learning Techniques (MLT) for Heart Disease Prediction,” Computational and Mathematical Methods in Medicine, vol. 2023, no. 1, Jan. 2023, doi: 10.1155/2023/8191261.
K. B. Sk, R. D, S. S. Priya, L. Dalavi, S. S. Vellela, and V. R. B, “Coronary Heart Disease Prediction and Classification using Hybrid Machine Learning Algorithms,” 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA), vol. 7, pp. 1–7, Mar. 2023, doi: 10.1109/icidca56705.2023.10099579.
D. V. S. Kumar, R. Chaurasia, A. Misra, P. K. Misra, and A. Khang, “Heart Disease and Liver Disease Prediction Using Machine Learning,” Data-Centric AI Solutions and Emerging Technologies in the Healthcare Ecosystem, pp. 205–214, Aug. 2023, doi: 10.1201/9781003356189-13.
B. L. R, S. Murugan, and M. Balakrishnan, “Detecting Alzheimer’s Disease Using Deep Learning Framework for Medial IoT Application,” EAI/Springer Innovations in Communication and Computing, pp. 101–123, 2024, doi: 10.1007/978-3-031-53972-5_5.
R. Rone Sarra, A. Musa Dinar, and M. Abed Mohammed, “Enhanced accuracy for heart disease prediction using artificial neural network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 29, no. 1, p. 375, Jan. 2022, doi: 10.11591/ijeecs.v29.i1.pp375-383.
A. B. Naeem et al., “Heart Disease Detection Using Feature Extraction and Artificial Neural Networks: A Sensor-Based Approach,” IEEE Access, vol. 12, pp. 37349–37362, 2024, doi: 10.1109/access.2024.3373646.
A. Pandey, B. A. Shivaji, M. Acharya, and K. K. Mohbey, “Mitigating class imbalance in heart disease detection with machine learning,” Multimedia Tools and Applications, Jun. 2024, doi: 10.1007/s11042-024-19705-8.
M. Bhanurangarao and R. Mahaveerakannan, “Improving Skin Lesion Diagnosis: Hybrid Blur Detection for Accurate Dermatological Image Analysis,” Advancements in Smart Computing and Information Security, pp. 225–240, 2024, doi: 10.1007/978-3-031-59097-9_17.
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.
B. A. Tama, S. Im, and S. Lee, “Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble,” BioMed Research International, vol. 2020, pp. 1–10, Apr. 2020, doi: 10.1155/2020/9816142.
C. Andersson, A. D. Johnson, E. J. Benjamin, D. Levy, and R. S. Vasan, “70-year legacy of the Framingham Heart Study,” Nature Reviews Cardiology, vol. 16, no. 11, pp. 687–698, May 2019, doi: 10.1038/s41569-019-0202-5.
J. Appadurai., “Prediction of EV Charging Behavior using BOA-based Deep Residual Attention Network,” Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, vol. 40, no. 1, 2024, doi: 10.23967/j.rimni.2024.02.002.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors would like to thank to the reviewers for nice comments on the
manuscript.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or
analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and
agreed to the published version of the manuscript.
Corresponding author
Kayalvizhi K
Kayalvizhi K
Department of Electronics and Communication Engineering, Sri Sairam Engineering College, Sirukalathur, Tamil Nadu, India.
Open Access This article is licensed under a Creative Commons Attribution
NoDerivs is a more restrictive license. It allows you to redistribute the material commercially
or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no
derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Kayalvizhi K, Kanchana S, Silvia Priscila S, Padmavathy C, Banda SNV Ramana Murthy and Veeramani Thangavel, “Optimized CNN-BiLSTM with Attention: A High Performance Model for Predicting Heart Disease Using Cleveland and Framingham Datasets”, Journal of Machine and Computing, pp. 1195-1205, October 2024. doi:10.53759/7669/jmc202404110.
