A Machine Learning–Based Bibliometric Analysis of Global Research Trends on Loneliness in Older Adults

<ns3:p>Background Loneliness in the elderly presents considerable physical and emotional health hazards, constituting a significant public health issue. This study utilizes bibliometric analysis to investigate global research trends, collaborations, and emerging themes in the application of machine learning to mitigate elderly loneliness. Methods A thorough examination of the Scopus database was performed for publications spanning from January 1, 2014, to December 31, 2024. The analysis used R packages like Bibliometrix and VOSviewer to look at things like citation metrics, trends in publications, partnerships between institutions, keyword co-occurrences, and networks of co-authors. Results Findings indicate an increasing interest in machine learning methodologies to address loneliness among the elderly, with the United States, China, and Canada at the forefront of publication output. Social media usage, depression, and predictive modeling were among the new themes that emerged, along with important writers and research groups. Conclusions This study gives us important information about how ML research for the well-being of older people is changing and can help us come up with new ways to reduce loneliness in older people.</ns3:p>