Analysis and Mapping of Machine Learning in the Context of Diabetes.

Background and Aims: The application of machine learning (ML) has started to change some important aspects of health care in diabetes. We aimed to utilize a bibliometric approach to analyze and map ML in the context of diabetes. Methods: To build our data set, we searched from the Web of Science Core Collection (WoSCC) database, and restricted our search from January 1, 2010 to December 31, 2023. For citation analysis, the online services of WoS were used to investigate the information content of the data set, VOSviewer and Microsoft Excel 2013 were employed to construct and visualize the bibliographic data. Results: Overall, 5,222 results that met the criteria were retrieved. The trend of published studies indicates that the number of publications has steadily increased over the past 14 years. The most active country was found to be USA, followed by the China and India. The highest level of cooperation with other countries belonged to the USA. The most prolific author on ML in the context of diabetes was Tien Yin Wong, with twenty-two articles affiliated at Tsinghua University; after that, Pantelis Georgiou with twenty articles affiliated at the Imperial College London, and Pau Herrero, with nineteen articles affiliated at Tijuana Institute of Technology. The most prolific research areas were machine learning, prediction models, diabetic retinopathy, deep learning, and diagnostics. Conclusion: The results of this study are a rich scientific source of ML for diabetes to guide researchers. This study can guide policymakers, physicians, and practitioners to help in the decision-making process. In addition, the findings will be useful for governments to guide future budgets for target studies.