<![CDATA[Chronic Kidney Disease (CKD) is a significant global health challenge that necessitates early diagnosis to prevent severe organ failure. While machine learning techniques such as Naive Bayes (NB) have been widely implemented for medical classification, their performance is often hindered by redundant and irrelevant features within high-dimensional medical datasets. This study aims to address this limitation by reducing the dimensions of non-contributing medical attributes, thereby minimizing bias and improving classification accuracy. Consequently, this study proposes the integration of Genetic Algorithm (GA) as a feature selection method to optimize the performance of the Naive Bayes (NB) algorithm in diagnosing CKD. The dataset, sourced from the UCI Machine Learning Repository, consists of 400 samples and 24 clinical features. A genetic algorithm was employed to identify the optimal feature subset through a binary evolution mechanism, while NB served both as the classifier and the fitness evaluation function. The results demonstrate that GA successfully reduced the data dimensions by 50%, streamlining the initial 24 features into 12 highly discriminative ones. Evaluation using 10-Fold Cross-Validation revealed a significant increase in accuracy, rising from 92.50% using the standard NB to 98.50% with the integrated GA-NB model. Furthermore, the recall reached 98.40%, indicating the model's high capability in minimizing diagnostic errors for affected patients (false negatives). This research proves that GA-based feature selection effectively enhances diagnostic reliability and model efficiency, presenting substantial potential for implementation in clinical decision support systems for medical professionals.]]>
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