A Bibliometric Analysis of Heart Disease Detection using Artificial Intelligence Techniques: Trends, Influential Works, and Research Gaps

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.