论文标题
基于机器学习的疾病诊断：文献计量分析
Machine Learning-Based Disease Diagnosis:A Bibliometric Analysis
论文作者
论文摘要
机器学习（ML）吸引了研究人员和从业人员，作为一种新的疾病诊断工具。随着ML的进步以及该领域的论文和研究的扩散，需要对基于机器学习的疾病诊断（MLBDD）进行完整检查。从文献计量学的角度来看，本文从2012年至2021年全面研究了MLBDD论文。因此，通过特定的关键字，已从Scopus和Web of Science（WOS）数据库中提取了1710篇文章，并集成到Excel DataSheet中以进行进一步分析。首先，我们根据年度出版物以及生产力最高的国家/地区，机构和作者研究出版物结构。其次，使用R-Studio软件可视化国家/地区，机构，作者和文章的共同引文网络。从引文结构和最具影响力的结构方面进一步研究了它们。本文为对该主题感兴趣的研究人员提供了MLBDD的概述，并对有兴趣在该领域进行更多研究的人进行了全面而完整的研究。
Machine Learning (ML) has garnered considerable attention from researchers and practitioners as a new and adaptable tool for disease diagnosis. With the advancement of ML and the proliferation of papers and research in this field, a complete examination of Machine Learning-Based Disease Diagnosis (MLBDD) is required. From a bibliometrics standpoint, this article comprehensively studies MLBDD papers from 2012 to 2021. Consequently, with particular keywords, 1710 papers with associate information have been extracted from the Scopus and Web of Science (WOS) database and integrated into the excel datasheet for further analysis. First, we examine the publication structures based on yearly publications and the most productive countries/regions, institutions, and authors. Second, the co-citation networks of countries/regions, institutions, authors, and articles are visualized using R-studio software. They are further examined in terms of citation structure and the most influential ones. This article gives an overview of MLBDD for researchers interested in the subject and conducts a thorough and complete study of MLBDD for those interested in conducting more research in this field.
