Cardiovascular Disorder Detection with a PSO-Optimized Bi-LSTM Recurrent Neural Network Model
Article Sidebar
Main Article Content
Abstract
The medical community is facing ever-increasing difficulties in identifying and treating cardiovascular diseases. The World Health Organization (WHO) reports that despite the availability of numerous high-priced medical remedies for persons with heart problems, CVDs continue to be the main cause of mortality globally, accounting for over 21 million deaths annually. When cardiovascular diseases are identified and treated early on, they cause far fewer deaths. Deep learning models have facilitated automated diagnostic methods for early detection of these diseases. Cardiovascular diseases often present insidious symptoms that are difficult to identify in a timely manner. Prompt diagnosis of individuals with CVD and related conditions, such as high blood pressure or high cholesterol, is crucial to initiate appropriate treatment. Recurrent neural networks (RNNs) with gated recurrent units (GRUs) have recently emerged as a more advanced variant, capable of surpassing Long Short-Term Memory (LSTM) models in several applications. When compared to LSTMs, GRUs have the advantages of faster calculation and less memory usage. When it comes to CVD prediction, the bio-inspired Particle Swarm Optimization (PSO) algorithm provides a straightforward method of getting the best possible outcomes with minimal effort. This stochastic optimization method requires neither the gradient nor any differentiated form of the objective function and emulates the behaviour and intelligence of swarms. PSO employs a swarm of agents, called particles, that navigate the search space to find the best prediction type.This study primarily focuses on predicting cardiovascular diseases using effective feature selection and classification methods. For CVD forecasting, we offer a GRU model built on recurrent neural networks and optimized with particle swarms (RNN-GRU-PSO). We find that the proposed model significantly outperforms the state-of-the-art models (98.2% accuracy in predicting cardiovascular diseases) in a head-to-head comparison.
Article Details
References
. Q. -J. Lv et al., "A Multi-Task Group Bi-LSTM Networks Application on Electrocardiogram Classification," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 8, pp. 1-11, 2020, Art no. 1900111, doi: 10.1109/JTEHM.2019.2952610.
. M. Fetanat, M. Stevens, P. Jain, C. Hayward, E. Meijering and N. H. Lovell, "Fully Elman Neural Network: A Novel Deep Recurrent Neural Network Optimized by an Improved Harris Hawks Algorithm for Classification of Pulmonary Arterial Wedge Pressure," in IEEE Transactions on Biomedical Engineering, vol. 69, no. 5, pp. 1733-1744, May 2022, doi: 10.1109/TBME.2021.3129459.
. T. I. Alshwaheen, Y. W. Hau, N. Ass’Ad and M. M. Abualsamen, "A Novel and Reliable Framework of Patient Deterioration Prediction in Intensive Care Unit Based on Long Short-Term Memory-Recurrent Neural Network," in IEEE Access, vol. 9, pp. 3894-3918, 2021, doi: 10.1109/ACCESS.2020.3047186.
. S. B. Shuvo, S. N. Ali, S. I. Swapnil, M. S. Al-Rakhami and A. Gumaei, "CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings," in IEEE Access, vol. 9, pp. 36955-36967, 2021, doi: 10.1109/ACCESS.2021.3063129.
. Y. An, K. Tang and J. Wang, "Time-Aware Multi-Type Data Fusion Representation Learning Framework for Risk Prediction of Cardiovascular Diseases," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 6, pp. 3725-3734, 1 Nov.-Dec. 2022, doi: 10.1109/TCBB.2021.3118418.
. Y. An, N. Huang, X. Chen, F. Wu and J. Wang, "High-Risk Prediction of Cardiovascular Diseases via Attention-Based Deep Neural Networks," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 3, pp. 1093-1105, 1 May-June 2021, doi: 10.1109/TCBB.2019.2935059.
. E. Longato, G. P. Fadini, G. Sparacino, A. Avogaro, L. Tramontan and B. Di Camillo, "A Deep Learning Approach to Predict Diabetes’ Cardiovascular Complications From Administrative Claims," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 9, pp. 3608-3617, Sept. 2021, doi: 10.1109/JBHI.2021.3065756.
. H. Zheng, S. W. A. Sherazi and J. Y. Lee, "A Stacking Ensemble Prediction Model for the Occurrences of Major Adverse Cardiovascular Events in Patients With Acute Coronary Syndrome on Imbalanced Data," in IEEE Access, vol. 9, pp. 113692-113704, 2021, doi: 10.1109/ACCESS.2021.3099795.
. P. Ghosh et al., "Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques," in IEEE Access, vol. 9, pp. 19304-19326, 2021, doi: 10.1109/ACCESS.2021.3053759.
. X. Zhang, D. Wu, F. Miao, H. Liu and Y. Li, "Personalized Hemodynamic Modeling of the Human Cardiovascular System: A Reduced-Order Computing Model," in IEEE Transactions on Biomedical Engineering, vol. 67, no. 10, pp. 2754-2764, Oct. 2020, doi: 10.1109/TBME.2020.2970244.
. D. Xu, J. Q. Sheng, P. J. -H. Hu, T. -S. Huang and C. -C. Hsu, "A Deep Learning–Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 2260-2272, June 2021, doi: 10.1109/JBHI.2020.3033323.
. M. Fetanat et al., "A physiological control system for an implantable heart pump that accommodates for interpatient and intrapatient variations", IEEE Trans. Biomed. Eng., vol. 67, no. 4, pp. 1167-1175, Apr. 2020.
. M. Fetanat et al., "A sensorless control system for an implantable heart pump using a real-time deep convolutional neural network", IEEE Trans. Biomed. Eng., vol. 68, no. 10, pp. 3029-3038, Oct. 2021.
. J. Wang, "A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network", Futur. Gener. Comput. Syst., vol. 102, pp. 670-679, 2020.
. B. AlizadehKharazi and A. H. Behzadan, "Flood depth mapping in street photos with image processing and deep neural networks", Comput. Environ. Urban Syst., vol. 88, 2021.
. U. B. Baloglu et al., "Classification of myocardial infarction with multi-lead ECG signals and deep CNN", Pattern Recognit. Lett., vol. 122, pp. 23-30, 2019.
. A.Bourouhou, A. Jilbab, C. Nacir and A. Hammouch, "Heart sounds classification for a medical diagnostic assistance", Int. J. Online Biomed. Eng., vol. 15, no. 11, pp. 88-103, 2019.
. Q.-U.-A. Mubarak, M. U. Akram, A. Shaukat and A. Ramazan, "Quality assessment and classification of heart sounds using PCG signals" in Applications of Intelligent Technologies in Healthcare, Cham, Switzerland:Springer, pp. 1-11, 2019.
. O. Deperlioglu, U. Kose, D. Gupta, A. Khanna and A. K. Sangaiah, "Diagnosis of heart diseases by a secure Internet of health things system based on autoencoder deep neural network", Comput. Commun., vol. 162, pp. 31-50, Oct. 2020.
. B. Xiao, Y. Xu, X. Bi, J. Zhang and X. Ma, "Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption", Neurocomputing, vol. 392, pp. 153-159, Jun. 2020.
. M. S. Oh and M. H. Jeong, "Sex differences in cardiovascular disease risk factors among Korean adults", Korean J. Med., vol. 95, no. 4, pp. 266-275, Aug. 2020.
. Brian Moore, Peter Thomas, Giovanni Rossi, Anna Kowalska, Manuel López. Exploring Natural Language Processing for Decision Science Applications. Kuwait Journal of Machine Learning, 2(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/217
. A.U. Haq, J. P. Li, M. H. Memon, S. Nazir and R. Sun, "A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms", Mobile Inf. Syst., vol. 2018, pp. 1-21, Dec. 2018.
. M. S. Amin, Y. K. Chiam and K. D. Varathan, "Identification of significant features and data mining techniques in predicting heart disease", Telematics Informat., vol. 36, pp. 82-93, Mar. 2019.
. M. Ashraf, S. M. Ahmad, N. A. Ganai, R. A. Shah, M. Zaman, S. A. Khan, et al., Prediction of Cardiovascular Disease Through Cutting-Edge Deep Learning Technologies: An Empirical Study Based on TENSORFLOW PYTORCH and KERAS, Singapore:Springer, pp. 239-255, 2021.
. D. Mienye, Y. Sun and Z. Wang, "An improved ensemble learning approach for the prediction of heart disease risk", Informat. Med. Unlocked, vol. 20, Jan. 2020