Mapping Knowledge Landscapes and Emerging trends in Machine Learning for Cardiovascular Disease: A Bibliometric and Visualization Analysis

Abstract

Objective: To understand the current status of the application of artificial intelligence in various research areas of cardiovascular diseases, and to identify the main research directions and technical approaches; secondly, to analyze the key themes and research hotspots involved in the existing literature, and to provide valuable references for further research. Methods: The literature was analyzed by searching the Web of Science database for relevant literature published from its inception to September 2024, using the bibliometrics software Citespace (6.2.R6), VOSviewer (1.6.20), and R (4.4.1) to analyze the literature econometrically. Results: A total of 996 literatures were selected, and distributed in 295 journals, the number of published papers showed an overall upward trend. The hot keywords were machine learning, natural language processing, etc. These technologies play an increasingly important role in the early diagnosis of cardiovascular diseases, risk prediction, and the formulation of personalized treatment plans. The study also found that some countries and regions have high scientific research output and influence in this field, such as the United States, China, and the United Kingdom, and scientific research institutions in these countries have led the cutting-edge work of artificial intelligence in cardiovascular disease research. Conclusion: The application of artificial intelligence in cardiovascular disease is in a rapid development stage. It has dramatically improved the accuracy and efficiency of early diagnosis, risk assessment, and personalized treatment, promoting the rapid development of precision medicine.

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