Научная визуализация, 2024, том 16, номер 5, страницы 1 - 21, DOI: 10.26583/sv.16.5.01
Visualizing the Impact of Machine Learning on Cardiovascular Disease Prediction: A Comprehensive Analysis of Research Trends
Авторы: Jeena Joseph1,A,B, K Kartheeban2,C
A Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India
B Department of Computer Applications, Marian College Kuttikkanam Autonomous, Idukki, Kerala, India
C Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India
1 ORCID: 0000-0003-4070-5868, jeenajoseph@mariancollege.org
2 ORCID: 0000-0002-0001-3376, k.kartheeban@klu.ac.in
Аннотация
Cardiovascular diseases (CVDs) continue to have a negative impact on global health, which highlights the need for accurate and efficient prediction methods. Machine learning (ML) techniques as tools for forecasting CVD has recently showed potential. This paper presents a comprehensive analysis of research trends in the field, focusing on visualizing the impact of ML in cardiovascular disease prediction. We used data visualization techniques to identify patterns and trends in an extensive database of scholarly publications on this subject that were published in Scopus between 1991 and 2023. The analysis reveals a substantial growth in research output, demonstrating the growing demand for ML-based CVD prediction. It reveals essential stakeholders and potential collaborators while highlighting the institutions and authors who have contributed most to this domain. The study also identifies high-impact journals that have published significant research in this domain, facilitating researchers in selecting appropriate outlets for dissemination. The study helps researchers identify the most critical areas for further research and fosters cooperation among subject-matter experts by offering insightful information about machine learning-based cardiovascular disease prediction development. The data is analyzed using the tools VOSviewer and Biblioshiny.
Ключевые слова: Bibliometric analysis, Cardiovascular disease, Heart disease prediction, Machine Learning, Biblioshiny, VOSviewer.
