Improving Diabetes Prediction Accuracy in Indonesia: A Comparative Analysis of SVM, Logistic Regression, and Naive Bayes with SMOTE and ADASYN

This study aims to enhance the accuracy of diabetes prediction models in Indonesia by comparing the performance of Support Vector Machines (SVM), Logistic Regression, and Naïve Bayes algorithms, both with and without synthetic oversampling techniques such as SMOTE and ADASYN. The research addresses the issue of imbalanced datasets in medical diagnostics, specifically in predicting diabetes among Indonesian patients, where such imbalance often leads to biased predictions. A comprehensive dataset comprising 657 patient records from a Regional General Hospital in Indonesia was used, with 70% of the data allocated for training and 30% for testing. The results indicate that the SVM model combined with SMOTE achieved the highest accuracy of 95.8% and an AUC of 99.1, underscoring the effectiveness of these techniques in improving prediction performance. The findings of this study highlight the importance of selecting appropriate oversampling methods and algorithms to optimize diabetes prediction accuracy in the Indonesian context, providing valuable insights for future healthcare strategies.