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Analyzing Diabetes Detection and Classification: A Bibliometric Review (2000-2023).

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This bibliometric analysis of diabetes detection and classification research from 2000-2023 reveals leading countries and trending keywords like machine learning and deep learning.

Keywords:
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Area of Science:

  • * Medical Informatics
  • * Computational Biology
  • * Public Health

Background:

  • * Diabetes mellitus remains a global health challenge, necessitating advanced detection and classification methods.
  • * Academic research in this domain has expanded significantly over the past two decades.
  • * Understanding research trends and contributions is crucial for future advancements.

Purpose of the Study:

  • * To conduct a bibliometric analysis of academic research on diabetes detection and classification.
  • * To identify key trends, influential countries, and prevalent research topics from 2000 to 2023.
  • * To map the scientific landscape and assess the impact of research in this field.

Main Methods:

  • * Bibliometric analysis utilizing the Web of Science database.
  • * Adherence to the PRISMA 2020 framework for study selection.
  • * Application of performance analysis and science mapping techniques on 863 selected publications.

Main Results:

  • * India, China, and the United States lead in publications and citations related to diabetes detection and classification.
  • * Machine learning, diabetic retinopathy, and deep learning are the most frequent keywords.
  • * Prevailing research topics include classification, diagnosis, and validation of diabetes.

Conclusions:

  • * The study provides a comprehensive overview of the academic research on diabetes detection and classification.
  • * Key global players and emerging technological trends in diabetes research are identified.
  • * Findings offer valuable insights for researchers, policymakers, and healthcare professionals in the field.