Cardiovascular disease prediction using hyperparameters-tuned LSTM considering COVID-19 with experimental validation

Kuna Dhananjay Rao; Mudunuru Satya Dev Kumar; Paidi Pavani; Darapureddy Akshitha; Kagitha Nagamaleswara Rao; Hafiz Tayyab Rauf; Mohamed Sharaf; Kuna Dhananjay Rao; Mudunuru Satya Dev Kumar; Paidi Pavani; Darapureddy Akshitha; Kagitha Nagamaleswara Rao; Hafiz Tayyab Rauf; Mohamed Sharaf

doi:10.3934/bioeng.2023017

AIMS Bioengineering

2023, Volume 10, Issue 3: 265-282. doi: 10.3934/bioeng.2023017

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Research article

Cardiovascular disease prediction using hyperparameters-tuned LSTM considering COVID-19 with experimental validation

1.
Department of Electrical and Electronics Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India
2.
Department of Electronics &Communication Engineering, Dhanekula Institute of Engineering and Technology, Vijayawada, India
3.
Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
4.
Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia

Received: 05 July 2023 Revised: 27 August 2023 Accepted: 05 September 2023 Published: 14 September 2023

Heart disease, globally recognized as a leading cause of death, has seen its impact magnified by the emergence of COVID-19. The heightened demand for early detection and diagnosis of heart disease has forced the development of innovative, intelligent systems. This research offers a novel approach by leveraging extended short-term memory networks (LSTM) and including COVID-19 as a significant parameter in cardiac arrest analysis. A comparative study is conducted between LSTM and other prevalent techniques, such as support vector machines (SVM), linear regression (LR), and artificial neural networks (ANN), focusing on accuracy and other prognostic criteria for heart disease. We aim to develop an intelligent system powered by LSTM to predict heart disease, thereby assisting healthcare professionals in making well-informed decisions about heart disease management, stroke prevention, and patient monitoring. Additionally, hyperparameter tuning has been performed to optimize the LSTM model's performance in cardiac arrest prediction. The results underscore that LSTM, especially when trained with COVID-19 as an input parameter, surpasses other established techniques in prediction accuracy. The proposed model underwent experimental testing, showcasing its proficiency in predicting cardiovascular disease.
- Machine learning,
- LSTM,
- cardiovascular diseases,
- electrocardiogram,
- hyperparameters
Citation: Kuna Dhananjay Rao, Mudunuru Satya Dev Kumar, Paidi Pavani, Darapureddy Akshitha, Kagitha Nagamaleswara Rao, Hafiz Tayyab Rauf, Mohamed Sharaf. Cardiovascular disease prediction using hyperparameters-tuned LSTM considering COVID-19 with experimental validation[J]. AIMS Bioengineering, 2023, 10(3): 265-282. doi: 10.3934/bioeng.2023017

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Abstract

Heart disease, globally recognized as a leading cause of death, has seen its impact magnified by the emergence of COVID-19. The heightened demand for early detection and diagnosis of heart disease has forced the development of innovative, intelligent systems. This research offers a novel approach by leveraging extended short-term memory networks (LSTM) and including COVID-19 as a significant parameter in cardiac arrest analysis. A comparative study is conducted between LSTM and other prevalent techniques, such as support vector machines (SVM), linear regression (LR), and artificial neural networks (ANN), focusing on accuracy and other prognostic criteria for heart disease. We aim to develop an intelligent system powered by LSTM to predict heart disease, thereby assisting healthcare professionals in making well-informed decisions about heart disease management, stroke prevention, and patient monitoring. Additionally, hyperparameter tuning has been performed to optimize the LSTM model's performance in cardiac arrest prediction. The results underscore that LSTM, especially when trained with COVID-19 as an input parameter, surpasses other established techniques in prediction accuracy. The proposed model underwent experimental testing, showcasing its proficiency in predicting cardiovascular disease.

Acknowledgments

The authors present their appreciation to King Saud University for funding this research through Researchers Supporting Program number (RSPD2023R704), King Saud University, Riyadh, Saudi Arabia.

Conflict of interest

The authors declare no conflict of interest.

Author Contributions:

The authors contributed equally on the completion of this manuscript.

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