<![CDATA[Diabetes mellitus is a chronic disease with a continuously increasing global prevalence. it is characterized by high blood glucose levels due to the body's inability to produce or effectively use insulin. the widespread impact on individuals and communities underscores the importance of early detection and proper management. In the digital era, data mining analysis has become a crucial tool in healthcare, enabling the exploration and analysis of health data on a large scale to identify patterns and trends that are difficult to detect manually. in the context of detecting diabetes mellitus, data mining holds great potential for predictive model development. one of the algorithms used is naïve bayes. This study analyzes naïve bayes classification for early symptoms of diabetes mellitus, with the aim of enhancing understanding of risk factors and developing early detection tools. The research findings indicate that naïve bayes has the highest accuracy of 78% with the application of missing value imputation mean. It is hoped that this research will enhance efforts in preventing and managing diabetes mellitus, as well as reducing the burden on individuals and communities as a whole.]]>
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