Fuzzy C-Means and K-Means Methods Comparison for the Detection of Diabetes - Subarja, Roy Efendi; Hendrik, Billy (Journal of Computer Science Advancements, 2024)
The necessity for precise and effective disease detection techniques has increased due to the rising incidence of diabetes. The main objective of this study is to assess how well the fuzzy C-Means and K-Means clustering algorithms detect diabetes. Based on pertinent medical data, the study attempts to examine how well these two clustering approaches identify cases of diabetes. For testing, a dataset with a variety of health and diagnostic indicator variables was used. Metrics including sensitivity, specificity, accuracy, and F1-score were used to evaluate the detection performance of the Fuzzy C-Means and K-Means algorithms that were used to cluster the dataset. Based on several evaluation criteria, the results show that both clustering approaches have promising potential for diabetes identification. However, their performance varies. This study sheds light on the advantages and disadvantages of clustering algorithms and advances our understanding of their suitability for diabetes identification. Improved diagnosis precision and early diabetes management intervention could result from more optimization and validation of these algorithms