| Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
|
|
|---|---|---|
| Article Number | 02009 | |
| Number of page(s) | 9 | |
| Section | Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202344802009 | |
| Published online | 17 November 2023 | |
Machine Learning Methods for Identification Osteoarthritis: A Bibliometric Analysis and General Review
1 Department of Information System, School of Postgraduates, Diponegoro University, Semarang, 50275, Central Java, INDONESIA
2 Department of Informatic, Faculty of Computer Science, University of Pembangunan Nasional “Veteran” Jawa Timur, Surabaya, 60294, East Java, INDONESIA
3 Department of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Semarang, 50275, Central Java, INDONESIA
4 Department of Anatomy, Faculty of Medicine, Diponegoro University, Semarang, 50275, Central Java, INDONESIA
* Corresponding author: faisalmuttaqin9669@students.undip.ac.id
This study describes machine learning trends in identifying osteoarthritis in different ways. To present visualizations, we performed bibliographic analysis using Vosviewer. Bibliographic data were collected via the Scopus database as of (2018-2023) and obtaining as many 46 journals. We found that one study identified osteoarthritis (OA) with reaching scores AUC > 0.95. In the last five years, United State and China having the highest rate of publication and index citation. The journal Arthritis and Rheumatology had the highest percentage of annual citations (89%) in 2018. Support vector machines (SVM) and LASSO regression were the most commonly used techniques by researchers.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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