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

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.