<![CDATA[Advances in data mining technology enable the use of classification algorithms to predict diseases based on patient data. Selecting the right algorithm is an important factor because it affects the accuracy and effectiveness of the prediction results. This study aims to analyze the performance comparison of three classification algorithms, namely Naïve Bayes, Decision Tree (C4.5), and Support Vector Machine (SVM) in predicting diseases using a health dataset. The research method used was a quantitative experiment with stages of data pre-processing, dataset division, algorithm application, and model evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The results show that the SVM algorithm provides the highest accuracy (88%), Decision Tree produces moderate accuracy (82%) with the advantage of interpretability, while Naïve Bayes is faster but less accurate (78%). These findings confirm that algorithm selection needs to consider both accuracy and ease of interpretation in supporting medical decision-making.]]>
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