<![CDATA[This study addresses the classification of balur therapy effectiveness as a complementary treatment using a machine learning approach, aiming to develop an accurate, balanced, and transparent model to support clinical decision-making. The methodology employs the Decision Tree algorithm, data imbalance handling using Synthetic Minority Oversampling Technique, and model interpretation through Local Interpretable Model-Agnostic Explanations. The dataset consists of 520 medical records, reduced to 478 after preprocessing, including data cleaning, binning, and outlier removal. The results indicate that the model without data balancing achieved the highest specificity of 0.8276 at a 90:10 split ratio, while the application of Synthetic Minority Oversampling Technique improved sensitivity toward the minority class but reduced specificity. Key influential features include occupation, diagnosis, and therapy duration. The interpretability analysis demonstrates that the model can clearly explain feature contributions to predictions. This study concludes that integrating classification, data balancing, and explainable modeling enhances medical data analysis. The findings imply strong potential for developing objective and transparent clinical decision support systems.]]>
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