Citation

Ferdaus, Jannatul and Rochy, Esmay Azam and Biswas, Uzzal and Tiang, Jun Jiat and Nahid, Abdullah-Al (2024) Analyzing Diabetes Detection and Classification: A Bibliometric Review (2000–2023). Sensors, 24 (16). p. 5346. ISSN 1424-8220

Text Analyzing Diabetes Detection and Classification_ A Bibliometric Review (2000–2023).pdf - Published Version Restricted to Repository staff only Download (5MB)

Abstract

Bibliometric analysis is a rigorous method to analyze significant quantities of bibliometric data to assess their impact on a particular field. This study used bibliometric analysis to investigate the academic research on diabetes detection and classification from 2000 to 2023. The PRISMA 2020 framework was followed to identify, filter, and select relevant papers. This study used the Web of Science database to determine relevant publications concerning diabetes detection and classification using the keywords “diabetes detection”, “diabetes classification”, and “diabetes detection and classification”. A total of 863 publications were selected for analysis. The research applied two bibliometric techniques: performance analysis and science mapping. Various bibliometric parameters, including publication analysis, trend analysis, citation analysis, and networking analysis, were used to assess the performance of these articles. The analysis findings showed that India, China, and the United States are the top three countries with the highest number of publications and citations on diabetes detection and classification. The most frequently used keywords are machine learning, diabetic retinopathy, and deep learning. Additionally, the study identified “classification”, “diagnosis”, and “validation” as the prevailing topics for diabetes identification. This research contributes valuable insights into the academic landscape of diabetes detection and classification.

Item Type:	Article
Uncontrolled Keywords:	Diabetes detection
Subjects:	R Medicine > RA Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Oct 2024 00:40
Last Modified:	01 Oct 2024 00:40
URII:	http://shdl.mmu.edu.my/id/eprint/12988

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