Evaluasi Performa Deteksi Penyakit Diabetes Dengan Fuzzy C-Means Dan K-Means Clustering
DOI:
https://doi.org/10.59061/jentik.v1i3.376Keywords:
Diabetes, Fuzzy C-Means, K-Means, ClusteringAbstract
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.
References
Ahamed, T., Muhaimin, S., & Uddin, M. M. (2020). Comparative Analysis of K-means and Fuzzy C-means Clustering Algorithms in Diabetic Retinopathy Detection. Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 6102-6108.
Alavi, A., & Ghaderi, R. (2019). Comparative analysis of k-means and fuzzy c-means algorithms for breast cancer data clustering. Journal of King Saud University-Computer and Information Sciences.
Barik, R., & Pradhan, B. B. (2020). Fuzzy C-means clustering technique for early diagnosis of diabetes. Procedia Computer Science, 167, 2743-2750.
Can, F., & Kandemir, M. (2020). A comparative analysis of K-means and fuzzy c-means clustering algorithms for classification of diabetic data. CBU International Conference Proceedings, 8, 1429-1433.
Poudel, R. P., & Jha, V. (2018). Comparative study of K-means and Fuzzy C-means clustering algorithms for medical data. 2018 International Conference on Computer and Applications (ICCA), 1-5.
Barik, R., & Pradhan, B. B. (2020). Fuzzy C-means clustering technique for early diagnosis of diabetes. Procedia Computer Science, 167, 2743-2750.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203.
Halkidi, M., Vazirgiannis, M., & Batistakis, Y. (2001). Clustering validity checking methods: Part I. ACM Sigmod Record, 30(2), 19-24.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.
Gopal, M., & Ranganathan, N. (Year). Diabetes Prediction using k-means clustering and naive Bayes classifier. Procedia Computer Science, Volume, Pages.
Dabas, S., & Agarwal, S. (Year). Comparative Analysis of K-means and Fuzzy C-means Clustering for Medical Datasets. Procedia computer science, Volume, Pages
Silva, D. F., Rodrigues, J. J., & de Albuquerque, V. H. C. (Year). Application of computational intelligence algorithms in diabetes diagnosis: a review. Journal of medical systems, Volume, Issue, Pages.
