<![CDATA[Gallstone disease is a hepatobiliary disorder that requires accurate diagnosis to support timely management and reduce the risk of complications. The availability of clinical and laboratory data provides an opportunity to apply machine learning as a supporting approach for disease classification. This study aims to compare the performance of Support Vector Machine (SVM), Random Forest, and XGBoost algorithms for gallstone disease classification using the Gallstone Dataset from the UCI Machine Learning Repository. The contribution of this study is to provide a comparative evaluation of SVM, Random Forest, and XGBoost on the Gallstone Dataset by consistently applying feature selection, feature standardization, class imbalance handling, and cross-validation to obtain a more objective comparison of the three algorithms. The dataset consists of 319 samples; therefore, model evaluation needs to consider the limited sample size and the potential risk of overfitting, which may affect model generalization. The research stages include feature selection using the ANOVA F-Value, handling class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), feature standardization, model development, and validation using Stratified 5-Fold Cross Validation. Evaluation was conducted using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The results show that XGBoost achieved the best performance, with an accuracy of 79.00%, precision of 80.95%, recall of 75.32%, F1-score of 78.03%, and AUC of 85.04%. Random Forest achieved an accuracy of 78.06%, while SVM achieved 74.92%. Feature importance analysis indicated that C-Reactive Protein (CRP), Vitamin D, Hyperlipidemia, Bone Mass, and Aspartate Aminotransferase (AST) were among the attributes that contributed significantly to the classification. The results indicate that XGBoost outperformed SVM and Random Forest on the dataset used and has potential as a supporting approach for developing a gallstone disease classification system; however, evaluation using larger and more diverse datasets is required to confirm its generalization capability.]]>
