<![CDATA[ABSTRACT The prevalence of diabetes mellitus is increasing globally, nationally, and regionally, and most of them are type 2 diabetes mellitus, which can cause complications, economic losses, and death. The purpose of this study is to examine the analysis of determinants and predictions of type 2 diabetes mellitus using machine learning methods. This study uses the scoping review method to view, accumulate and synthesize the results of previous studies on the analysis of determinants and predictions of type 2 diabetes mellitus using machine learning methods. The inclusion criteria in this study were articles published in the indexed journal database PubMed, Google Scholar, Crossref in English and Indonesian, journals published in the 2017-2021 range and 15 articles that met the inclusion criteria. The search results were a total of 860 articles from 3 databases (PubMed, Google Scholar, Crossref) in which 98 of them were duplicate articles and were excluded. Of the remaining 762 articles, 142 were not full text and 605 were excluded after eligibility screening because they were irrelevant. The remaining 15 articles were systematically reviewed and qualitatively analyzed using the NVIVO-12 Plus application. From the analysis of previous studies concluded that age, obesity, family history of disease, and lack of physical activity are risk factors for type 2 diabetes mellitus, while the gender variable from the analysis of previous research shows that there is no significant relationship between gender and type 2 diabetes. With early prediction of type 2 diabetes mellitus preventive measures, treatment can be carried out immediately and reduce the incidence of complications that can worsen the condition of people with type 2 diabetes. ]]>
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