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/10.38124/ijisrt/IJISRT23NOV2413_
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
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.
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.
