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. 2025 Jul 25:12:1602077.
doi: 10.3389/fmed.2025.1602077. eCollection 2025.

Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024

Muhammad Junaid Akram  1   2 , Asad Nawaz  1   2 , Yuan Yuxing  1   2 , Jinpeng Zhang  1   2 , Huang Haixin  1   2 , Lingjuan Liu  1   2 , Xu Qian  3 , Jie Tian  1   2
Affiliations

Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024

Muhammad Junaid Akram et al. Front Med (Lausanne). .

Abstract

Background: Cardiomyopathy and heart failure are among the most critical challenges in modern cardiology, with increasing attention to the integration of machine learning (ML) and artificial intelligence (AI) for diagnostics, risk prediction, and therapeutic strategies. This study was aimed at evaluating global research trends, influential contributions, and emerging themes in the domain of cardiomyopathy and heart failure from 2005 to 2024.

Methodology: A comprehensive bibliometric analysis was conducted using the Web of Science Core Collection (WoSCC) database. The study utilized the R- package bibliometrix-biblioshiny, VOSviewer, Scimago Graphica and CiteSpace to analyze the publications on cardiomyopathy, heart failure, machine learning, and artificial intelligence. Key metrics examined included top institutions, countries, journals, keywords, co-authorship networks, and keyword co-occurrence patterns. Additionally, the analysis evaluated publication counts, citation trends, H-index, and collaboration metrics to identify research trends and emerging themes in the field.

Results: A total of 2,110 publications retrieved from the last 20 years were included in the analysis. The United States of America (USA), China, and the United Kingdom (UK), emerged as leading contributors, with institutions such as Mayo Clinic and Harvard University producing high-impact research. Dominant keywords included "heart failure," "risk," "diagnosis," and "artificial intelligence," reflecting the increasing reliance on ML for predictive analytics. Thematic evolution revealed a transition from traditional classification methods to advanced techniques, including feature selection and proteomics. Influential studies, including those by Friedman PA, Noseworthy PA, and Attia ZI, showcased the transformative potential of AI in cardiology. Global collaboration networks underscored strong partnerships but highlighted disparities in contributions from low-income regions.

Conclusion: This analysis highlights the dynamic evolution of cardiomyopathy research, emphasizing the critical role of ML and AI in advancing diagnostics and therapeutic strategies. Future research should address challenges in scalability, data standardization, and ethical considerations to ensure equitable access and implementation of these technologies, particularly in underrepresented regions.

Keywords: CiteSpace; VOSviewer; artificial intelligence; bibliometric; cardiomyopathy; heart failure; machine learning.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Flowchart depicting the process of bibliometric analysis. It starts with the identification of 2,846 records from the Web of Science core collection, followed by screening that excludes 736 records not related to cardiomyopathy, heart failure, or duplicates. This results in 2,110 records included in the analysis. Literature records are downloaded in plain text format. Three bibliometric analysis and visualization software tools are used: R with biblioshiny (for citations, keywords, authors, and thematic evolution), Citespace (for keyword and cited reference citation bursts), and VOSviewer (for co-authorship and keyword co-occurrence analysis).
FIGURE 1
Bibliometric flow chart.
Bar chart depicting the number of publications and citations from 2005 to 2025. Bars, representing publications, increase steadily in height, peaking in either 2024 or 2025. A line represents citations, showing a sharp rise starting in 2018, peaking in 2024, then declining in 2025.
FIGURE 2
Annual number of publications and citations.
World map highlighting several countries with associated numerical values. Key countries and values include the USA (290), Canada (160), the United Kingdom (332), China (232), and India (104). Other countries, such as Russia, Brazil, and Australia, have values 58, 78, and 92, respectively. Different colors are used to distinguish countries.
FIGURE 3
Country-wise affiliation count of publications.
Graphical analysis showing research connections and productivity: A) and B) display network maps of collaborations among researchers. C) is a bar chart listing top affiliations by the number of articles, with Harvard University leading. D) is a curve illustrating author productivity distribution according to Lotka’s Law. E) is a network map of institutional collaborations.
FIGURE 4
(A) Author-co-author network visualization. (B) Author co-author overlay visualization. (C) Top 10 relevant affiliation. (D) Author’s productivity through Lotka’s Law. (E) Institutional co-authorship analysis.
A set of four related visual data representations. Panel A shows a bar chart of the top ten most occurred keywords, with “heart-failure” appearing most frequently at 481 occurrences. Panel B and C display network graphs with keywords like “heart-failure,” “mortality,” and “disease” prominently featured in various colors, indicating relationships between terms. Panel D is a flow diagram showing connections between institutions, keywords, and countries, with entities such as “machine learning” and “heart failure” linked to countries like the USA and China.
FIGURE 5
(A) Top occurred keywords. (B) Keywords co-occurrence network visualization. (C) Keywords co-occurrence overlay visualization. (D) Three-field plot of keyword analysis. [Left field: institutions (AU-UN); Middle field: keywords (DE); Right field: countries (AU-CO)].
Panel A displays a bar chart titled “Top 15 Keywords with the Strongest Citation Bursts,” listing keywords like “cardiac resynchronization therapy” and their citation strengths from 2005 to 2024. Panel B shows a similar bar chart titled “Top 15 References with the Strongest Citation Bursts,” listing references with citation strengths over the same period. Both panels use colored bars to indicate durations of high citation activity.
FIGURE 6
(A) Top 15 keywords with the strongest citation bursts. (B) Top 15 references with the strongest citation bursts.
Sankey diagram illustrating trends in medical research topics from 2005 to 2025. Key topics include machine learning, artificial intelligence, and cardiovascular diseases. The flow of topics shifts over time, highlighting evolving research focuses. Notable mentions are atrial fibrillation, heart failure, and precision medicine in later years.
FIGURE 7
Thematic evolution map.
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