K-Means and Fuzzy C-Means Cluster Food Nutrients for Innovative Diabetes Risk Assessment

irma darmayanti, Dinar Mustofa, Nurul Hidayati, Inka Saputri

Abstract


Packaged food and beverages often pose a risk of increasing diabetes when consumed regularly. This study aims to classify these products based on their nutritional content listed on the labels, with a focus on identifying diabetes risk. The methods employed include K-Means and Fuzzy C-Means, K-Means is used to determine initial center of cluster, while Fuzzy C-Means enhances the clustering by assigning probabilistic memberships to each data point. These methods are applied to products sold in stores in Banyumas Regency, Central Java, Indonesia. This research is the first to combine these two methods in the context of product clustering based on nutritional labels. The results indicate that packaged food and beverage products can be classified into high-risk and low-risk clusters for diabetes. Consequently, this study provides important guidance for consumers in choosing healthier.

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References


R. Birjais, A. K. Mourya, R. Chauhan, and H. Kaur, “Prediction and diagnosis of future diabetes risk: a machine learning approach,” SN Appl Sci, vol. 1, no. 9, Sep. 2019, doi: 10.1007/s42452-019-1117-9.

L. Wang, X. Wang, A. Chen, X. Jin, and H. Che, “Prediction of type 2 diabetes risk and its effect evaluation based on the xgboost model,” Healthcare (Switzerland), vol. 8, no. 3, 2020, doi: 10.3390/healthcare8030247.

O. : Marendra, S. Kartolo, and A. H. Santoso, “Hubungan Frekuensi Konsumsi, Asupan Energi, Lemak, Gula, dan Garam dalam Fast Food dengan Kejadian Obesitas pada Siswa/I SMP X Yogyakarta,” EBERS PAPYRUS, vol. 28, no. 1, p. 38, 2022.

U. e. Laila, K. Mahboob, A. W. Khan, F. Khan, and W. Taekeun, “An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study,” Sensors, vol. 22, no. 14, Jul. 2022, doi: 10.3390/s22145247.

L. Paper et al., “Cross-sectional comparisons of dietary indexes underlying nutrition labels: nutri-score, Canadian ‘high in’ labels and Diabetes Canada Clinical Practices (DCCP),” Eur J Nutr, vol. 62, no. 1, pp. 261–274, Feb. 2023, doi: 10.1007/s00394-022-02978-w.

D. Westari, “Performa Comparison of the K-Means Method for Classification in Diabetes Patients Using Two Normalization Methods,” International Journal of Multidisciplinary Research and Analysis, vol. 04, no. 01, Jan. 2021, doi: 10.47191/ijmra/v4-i1-03.

Simeftiany Indrilemta Lomo, Endang Darmawan, and Sugiyarto, “Cluster analysis of type II Diabetes Mellitus Patients with the Fuzzy C-means method,” Annals of Mathematical Modeling, vol. 3, no. 1, pp. 24–31, Jun. 2023, doi: 10.33292/amm.v3i1.28.

S. Surono and E. Darmawan, “The Risk Cluster in Type 2 Diabetes Mellitus Based on Risk Parameters Using Fuzzy C-Means Algorithm,” 2023. [Online]. Available: https://doi.org/11.26554/sti.2223.8.1.17-24

H. Naz, T. Saba, F. S. Alamri, A. S. Almasoud, and A. Rehman, “An Improved Robust Fuzzy Local Information K-Means Clustering Algorithm for Diabetic Retinopathy Detection,” IEEE Access, vol. 12, pp. 78611–78623, 2024, doi: 10.1109/ACCESS.2024.3392032.

S. Kusumadewi, L. Rosita, and E. G. Wahyuni, “Performance of Fuzzy C-Means (FCM) and Fuzzy Subtractive Clustering (FSC) on Medical Data Imputation,” ComTech: Computer, Mathematics and Engineering Applications, vol. 15, no. 1, pp. 29–40, May 2024, doi: 10.21512/comtech.v15i1.11002.

Z. Chen et al., “Ultra-Processed Food Consumption and Risk of Type 2 Diabetes: Three Large Prospective U.S. Cohort Studies,” Diabetes Care, vol. 46, no. 7, pp. 1335–1344, Jul. 2023, doi: 10.2337/dc22-1993.

M. Mehedi Hassan, S. Mollick, and F. Yasmin, “An unsupervised cluster-based feature grouping model for early diabetes detection,” Healthcare Analytics, vol. 2, Nov. 2022, doi: 10.1016/j.health.2022.100112.

H. Thakkar, V. Shah, H. Yagnik, and M. Shah, “Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis,” Jan. 01, 2021, KeAi Communications Co. doi: 10.1016/j.ceh.2020.11.001.

K. El Moutaouakil, A. Yahyaouy, S. Chellak, and H. Baizri, “An Optimized Gradient Dynamic-Neuro-Weighted-Fuzzy Clustering Method: Application in the Nutrition Field,” International Journal of Fuzzy Systems, vol. 24, no. 8, pp. 3731–3744, Nov. 2022, doi: 10.1007/s40815-022-01358-0.

R. Gestavito, A. Id Hadiana, F. Rakhmat Umbara, and U. Jenderal Achmad Yani Jl Terusan Jenderal Sudirman, “Pengelompokan Tingkat Risiko Penyakit Diabetes Melitus Menggunakan Algoritma K-Means Clustering,” Jurnal Masyarakat Informatika Unjani, vol. 8, no. 1, pp. 16–35, 2024.

I. Tri Gustiane and T. Suprapti, “Clustering Hasil Cek Darah Diabetes Lansia Menggunakan Metode K-Means di Posbindu Kp. Lebakjero Desa Ciherang,” Jurnal Mahasiswa Teknik Informatika, vol. 8, no. 2, 2024.

N. Husna, F. Hanum, and M. F. Azrial, “Pengelompokkan Produk Kemasan yang Harus Dihindari Penderita Diabetes Menggunakan Algoritma K-Means Clustering,” InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan), vol. 4, no. 1, pp. 167–174, Sep. 2019, doi: 10.30743/infotekjar.v4i1.1484.

A. A. Jasim, L. R. Hazim, H. Mohammedqasim, R. Mohammedqasem, O. Ata, and O. H. Salman, “e-Diagnostic system for diabetes disease prediction on an IoMT environment-based hyper AdaBoost machine learning model,” Journal of Supercomputing, Jul. 2024, doi: 10.1007/s11227-024-06082-0.

S. Dörterler, H. Dumlu, D. Özdemir, and H. Temurtaş, “Hybridization of Meta-heuristic Algorithms with K-Means for Clustering Analysis: Case of Medical Datasets,” Gazi Journal of Engineering Sciences, vol. 10, no. 1, pp. 1–11, Apr. 2024, doi: 10.30855/gmbd.0705n01.

S. J. Setu, F. Tabassum, S. Jahan, and Md. I. Islam, “Detection of Diabetes using Combined ML Algorithm,” International Journal of Intelligent Systems and Applications, vol. 16, no. 1, pp. 11–23, Feb. 2024, doi: 10.5815/ijisa.2024.01.02.

O. Virgolici, B. Virgolici, and " Carol, “Diabetes Prediction Using Machine Learning Techniques: A Brief Overview Diabetes & its Complications,” vol. 8, p. 2024, 2024.




DOI: https://doi.org/10.32520/stmsi.v13i5.4552

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