<![CDATA[The increasing prevalence of diabetes has led to a growing need for accurate and efficient disease detection methods. This research focuses on evaluating the performance of diabetes detection using Fuzzy C-Means and K-Means clustering algorithms. The study aims to compare the effectiveness of these two clustering techniques in identifying diabetes cases based on relevant medical data. A dataset comprising various health parameters and diagnostic indicators was utilized for experimentation. The Fuzzy C-Means and K-Means algorithms were implemented to cluster the dataset, and their detection performance was assessed using metrics such as sensitivity, specificity, accuracy, and F1-score. The results indicate that both clustering methods exhibit promising potential for diabetes detection, with variations in their performance based on different evaluation criteria. This research contributes to a deeper understanding of the applicability of clustering algorithms in diabetes detection and provides insights into their strengths and limitations. Further optimization and validation of these algorithms could lead to enhanced diagnostic accuracy and early intervention in diabetes management.]]>
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