<![CDATA[Diabetes mellitus is a illness that significantly affects the global population. This study explores theimplementation of Naive Bayes to predict diabetes mellitus. The problems faced include the complexity ofclinical datasets, feature diversity, and the need for accurate predictions. The proposed solution is to usethe Naive Bayes classification algorithm that utilizes a simple but strong assumption about featureindependence. The system is described with steps involving data pre-processing, dataset partitioning,Naive Bayes model training, and performance evaluation. The datasets used include age, gender, weight,HbA1C, fasting blood sugar. The results and testing show that the Naive Bayes model can provide fairlyaccurate predictions for diabetes mellitus, with performance assessed through evaluation metrics such as ,recall, accuracy, F1-score, and precision. In conclusion, the implementation of Naive Bayes is a fairlyeffective approach to predicting diabetes mellitus. In this study, The performance of the Naïve Bayesmethod is considered quite accurate, this is evidenced in the highest accuracy score of 85.71%. Despite itssimplicity, this algorithm can handle the complexity of the dataset and provide reliable predictions.Keywords: Diabetes Mellitus, Data Mining, Machine Learning, Classification, and Naïve Bayes.]]>
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