<![CDATA[Chronic kidney disease is a serious illness that requires early diagnosis to improve treatment outcomes, especially in the elderly. The main challenge in diagnosing this disease lies in the fact that symptoms often do not appear until the disease has reached an advanced stage, which necessitates the use of accurate prediction methods. Additionally, the dataset's limited size, consisting of only 195 patient records, may affect the algorithm's ability to identify patterns. Choosing the appropriate algorithm is also a challenge, as some algorithms have limitations in handling complex medical data. This study aims to evaluate the performance of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in predicting chronic kidney disease. The dataset was analyzed using Weka Waikato software and tested using the 9-fold cross-validation method. The best results were obtained using the Naïve Bayes algorithm, with an accuracy of 97.4359%. Based on these results, it can be concluded that both algorithms can be used to predict chronic kidney disease in the elderly. However, to further improve prediction accuracy, proposed solutions include expanding the dataset with more diverse data and optimizing the algorithm's hyperparameters. On the other hand, the Naïve Bayes algorithm demonstrated higher accuracy compared to KNN in this study, making it the more recommended choice.]]>
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