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Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis.

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Summary
This summary is machine-generated.

Machine learning and deep learning for diabetes research show rapid growth, with detection being the most studied area. This field is rapidly evolving and is expected to continue its expansion in the coming years.

Keywords:
algorithmsartificial intelligencebibliometric analysisdeep learningdiabetesmachine learning

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Area of Science:

  • Diabetes research
  • Artificial Intelligence in Medicine
  • Bibliometrics

Background:

  • Machine learning (ML) and deep learning (DL) are increasingly utilized in diabetes research.
  • A comprehensive overview of the knowledge generation landscape in this field is lacking.
  • Bibliometric analysis can reveal global research trends and key players.

Purpose of the Study:

  • To conduct a bibliometric analysis of scientific articles on ML/DL in diabetes research from 2000-2022.
  • To identify global research trends, networks, and prominent contributors.
  • To highlight key topics, countries, institutions, and journals in the field.

Main Methods:

  • Scopus database was used for article retrieval.
  • Articles were categorized into detection, prediction, and management.
  • Biblioshiny and RStudio were employed for data analysis.

Main Results:

  • 1773 articles were analyzed, with significant publication and citation increases since 2012.
  • Detection emerged as the dominant research category, followed by prediction and management.
  • China, India, and the USA were the most productive countries; University of California was the most influential institution.

Conclusions:

  • The field of ML/DL in diabetes research is rapidly evolving with exponential growth.
  • Productivity has surged in recent years and is projected to continue.
  • Detection, prediction, and management are key areas of focus.