Cardiovascular diseases prediction by machine learning incorporation with deep learning

Sivakannan Subramani; Neeraj Varshney; M Vijay Anand; Manzoore Elahi M Soudagar; Lamya Ahmed Al-Keridis; Tarun Kumar Upadhyay; Nawaf Alshammari; Mohd Saeed; Kumaran Subramanian; Krishnan Anbarasu; Karunakaran Rohini

doi:10.3389/fmed.2023.1150933

Cardiovascular diseases prediction by machine learning incorporation with deep learning

Front Med (Lausanne). 2023 Apr 17:10:1150933. doi: 10.3389/fmed.2023.1150933. eCollection 2023.

Authors

Affiliations

¹ Department of Advanced Computing, St. Joseph's University, Bengaluru, Karnataka, India.
² Department of Computer Engineering and Applications, GLA University, Mathura, Uttar Pradesh, India.
³ Department of Mechanical Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India.
⁴ Department of VLSI Microelectronics, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India.
⁵ Faculty of Science, Princess Norah Bint Abdulrahman University, Riyadh, Saudi Arabia.
⁶ Department of Biotechnology, Parul Institute of Applied Sciences and Centre of Research for Development, Parul University, Vadodara, India.
⁷ Department of Biology, College of Science, University of Hail, Hail, Saudi Arabia.
⁸ Centre for Drug Discovery and Development, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
⁹ Department of Bioinformatics, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India.
¹⁰ Unit of Biochemistry, Centre of Excellence for Biomaterials Engeneering, Faculty of Medicine, AIMST University, Semeleing, Bedong, Malaysia.
¹¹ Centre for Excellence for Biomaterials Science AIMST University, Semeling, Bedong, Malaysia.
¹² Department of Computational Biology, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India.

Abstract

It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.

Keywords: AI-based technologies; cardiovascular disease; computational method; internet of things; machine learning.