Authors : Tanushree Bharti; Pushpendra Kanwar

Volume/Issue : Volume 8 - 2023, Issue 11 - November

Google Scholar : https://tinyurl.com/2ceu3fdn

Scribd : https://tinyurl.com/4uew8z5k

DOI : https://doi.org/10.5281/zenodo.10353213

Abstract : Advanced diagnostic techniques are required as cardiovascular diseases continue to pose a serious threat to global health. The scientific community has recently shown a great deal of interest in the application of deep learning techniques to the detection of heart disease. In order to synthesize the body of research on the use of deep learning in the detection of heart disease, this study provides a thorough bibliometric analysis. A wide variety of publications, including articles, conference papers, and reviews, are included in the analysis. These were obtained from Scopus and WoS databases. Total 662 documents are analyzed from these databases. The study looks at geographic distributions, historical trends, and influential figures in the field. We uncover key papers and authors through quantitative analyses, providing insight into the way research themes have changed over time. The study delves into co-authorship networks and institutional collaborations, offering valuable perspectives on the collaborative environment among scholars operating within this field. To find popular terms and hot topics, keyword analysis is used, which helps to provide a more sophisticated understanding of the main ideas guiding the research that is being done today.

Keywords : Bibliometric Analysis, Heart Diseases, Machine Learning, Deep Learning, Quantitative Analysis.