Machine Learning Methods for Identification Osteoarthritis: A Bibliometric Analysis and General Review

Abstract

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.