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分析糖尿病检测和分类：文献计量学综述（2000-2023）。

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

机构信息

Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.

Centre for Wireless Technology (CWT), Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia.

出版信息

Sensors (Basel). 2024 Aug 19;24(16):5346. doi: 10.3390/s24165346.

DOI:10.3390/s24165346
PMID:39205040
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11359783/
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.

摘要

文献计量分析是一种严谨的方法，可用于分析大量的文献计量数据，以评估其对特定领域的影响。本研究使用文献计量分析方法，调查了 2000 年至 2023 年期间糖尿病检测和分类的学术研究。本研究遵循 PRISMA 2020 框架，以确定、筛选和选择相关论文。本研究使用 Web of Science 数据库，使用“糖尿病检测”、“糖尿病分类”和“糖尿病检测和分类”等关键词，确定与糖尿病检测和分类相关的出版物。共选择了 863 篇论文进行分析。该研究应用了两种文献计量技术：绩效分析和科学图谱。使用各种文献计量参数，包括出版物分析、趋势分析、引文分析和网络分析，评估这些文章的表现。分析结果表明，印度、中国和美国是在糖尿病检测和分类方面发表论文和引文数量最多的前三个国家。使用最频繁的关键词是机器学习、糖尿病性视网膜病变和深度学习。此外，该研究还确定了“分类”、“诊断”和“验证”是糖尿病识别的主要主题。这项研究为糖尿病检测和分类的学术领域提供了有价值的见解。

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