Author
Listed:
- Fahad Alqurashi
(Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)
- Aasim Zafar
(Department of Computer Science, Aligarh Muslim University, Aligarh 202001, India)
- Asif Irshad Khan
(Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)
- Abdulmohsen Almalawi
(Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)
- Md Mottahir Alam
(Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)
- Rezaul Azim
(Department of Physics, University of Chittagong, Chattogram 4331, Bangladesh)
Abstract
Cardiovascular diseases (CVD) are amongst the leading causes of death worldwide. The Internet of Things (IoT) is an emerging technology that enables the healthcare system to identify cardiovascular diseases. In this article, a novel cardiovascular disease prediction framework combining Predator Crow Optimization (PCO) and Deep Neural Network (DNN) is designed. In the proposed PCO-DNN framework, DNN is used to predict cardiac disease, and the PCO is utilized to optimize the DNN parameters, thereby maximizing the prediction performances. The proposed framework aims to predict and classify cardiovascular diseases accurately. Further, an intensive comparative analysis is performed to validate the obtained results with the existing classification models. The results show that the proposed framework achieves an accuracy of 96.6665%, a precision of 97.5256%, a recall of 97.0953%, and an F1-measure of 96.4242% and can outperform the existing CVD predictors.
Suggested Citation
Fahad Alqurashi & Aasim Zafar & Asif Irshad Khan & Abdulmohsen Almalawi & Md Mottahir Alam & Rezaul Azim, 2023.
"Deep Neural Network and Predator Crow Optimization-Based Intelligent Healthcare System for Predicting Cardiac Diseases,"
Mathematics, MDPI, vol. 11(22), pages 1-29, November.
Handle:
RePEc:gam:jmathe:v:11:y:2023:i:22:p:4621-:d:1278318
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