用后勤回归模型对心脏病分类的科学文献进行文献识别分析和可视化
Neena Suresh1, Binu Thomas2, Jeena Joseph2
1Department of Computer Sciences, Mahatma Gandhi University, Kottayam, Kottayam, IND.
Cureus
|July 29, 2024
概括
这种对心脏病分类后勤回归的文献分析显示,中国是最具生产力的国家. 研究趋势显示,人们正在转向机器学习算法,如支持向量机器和k-最近邻居.
科学领域:
- * 医学信息学 医学信息学
- * 医疗保健中的机器学习
- * 图书统计学 (Bibliometrics) 是一个非常重要的领域.
背景情况:
- *人工智能和机器学习算法越来越多地用于临床预测.
- *逻辑回归是医学研究中受欢迎的机器学习算法,因为它的简单性和可解释性.
- *心脏病的分类是临床预测的关键领域.
研究的目的:
- * 通过逻辑回归来研究心脏病分类的研究生产力.
- *通过图书统计分析分析该研究领域的当前模式和未来趋势.
- * 确定有影响力的研究团体,国家和作者,并确定研究缺口.
主要方法:
- *从Scopus (2019-2023) 获取的2331份文档进行了图书统计分析.
- *使用了Biblioshiny和VOSviewer软件进行数据分析.
- *分析了引用数量,作者贡献,出版物数量和机构贡献.
主要成果:
- *中国是最具生产力的国家,拥有700份文件,其次是印度和美国.
- *哈佛医学院以六篇论文为机构做出了最大的贡献.
- * 王是最富有成效的作者,拥有73份文件.
- *趋势主题表明向支向量机,k-最近邻居和天真贝叶斯算法的进展.
结论:
- * 该研究提供了对心脏病分类后勤回归研究领域的见解.
- *确定了关键贡献者和新兴趋势,帮助研究人员和医疗保健专业人员分配资源.
- *限制包括从其他数据库中排除数据,并专注于最近的发展,不包括旧文献和其他强大的预测技术.
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