<![CDATA[Chronic Kidney Disease (CKD) is a global health issue characterized by low early detection rates and high diagnostic costs. Artificial intelligence, particularly machine learning, offers a promising solution as a rapid and cost-effective decision support system. This study aims to comprehensively analyze and compare the performance of two simple and interpretable classification algorithms, K-Nearest Neighbor (K-NN) and Naïve Bayes (NB), for predicting CKD based on clinical data. The dataset was sourced from the UCI Machine Learning Repository, comprising 400 instances and 25 clinical attributes such as blood pressure and serum creatinine. The methodology included data preprocessing (median imputation for numerical features, mode imputation for categorical features), encoding, Min-Max normalization, data splitting (70:30 ratio), model training, K parameter optimization for K-NN via 5-fold cross-validation, and evaluation using accuracy, precision, recall, F1-Score, and Confusion Matrix metrics. Experimental results demonstrated that the Naïve Bayes algorithm achieved superior performance with an accuracy of 95.83%, precision of 95.95%, recall of 97.26%, and F1-Score of 96.60%. The K-NN algorithm with an optimal K=5 attained an accuracy of 91.67%. Statistical analysis using a paired t-test (α=0.05) with p-value=0.012 confirmed that this performance difference was significant. It is concluded that Naïve Bayes is more effective for this CKD dataset, likely due to its robustness in handling feature independence assumptions and varied data scales. This model holds strong potential for development into an early-stage CKD screening tool to assist healthcare professionals.]]>
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