<![CDATA[Diabetes is a global health problem that is increasing in prevalence worldwide. This study compares the performance of two data analysis methods, namely binary logistic regression and naïve bayes classifier in predicting diabetes risk. This study aims to identify factors that significantly affect diabetes risk and classify diabetes risk using binary logistic regression, then compare the classification with the naive bayes classifier algorithm. Binary logistic regression models the relationship between independent predictor variables and binary dependent variables, while naïve bayes classifier uses the assumption of independence between variables. In this study, both methods were evaluated based on accuracy, sensitivity, specificity and positive predictive value. The results show that the factors that influence the risk of diabetes are Age, Gender, Polyuria, Polydipsia, Genital thrush, Itching, Irritability, and Partial paresis. Furthermore, the binary logistic regression results have a higher classification accuracy (92.31%) compared to the naïve bayes classifier (84.61%). Therefore, binary logistic regression was identified as the best method to predict diabetes risk in the context of this study]]>
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