Utilization of K-means Clustering for Classifying Diabetes Risk Populations According to Health Behaviors and 3Es-2Ss Health Literacy

Supaporn Yodmunee, Wongpanya S. Nuankaew, Thapanapong Sararat, Pratya Nuankew
Pages 983-998
Abstract Views 509
PDF Downloads 383
Crossref Citations -
DOI https://doi.org/10.47738/jads.v7i2.1042

Abstract


This study focused on classifying populations at risk for diabetes using K-means clustering integrated with the 3Es–2Ss health literacy framework: eating, exercise, emotion, smoking cessation, and alcohol cessation. Biological, behavioral, and health literacy data were analyzed. The dataset was collected from 126 participants identified as at-risk individuals in Ngao District, Lampang Province, Thailand. This relatively small, community-based sample provides valuable insights into local health behaviors but limits the generalizability and statistical power of the findings to broader populations. The K-means clustering analysis, guided by the Elbow method, identified k = 4 as the optimal number of clusters, yielding four distinct groups with different socio-demographic and health characteristics. These clusters revealed variations in health profiles, economic status, and behavioral literacy within the Thai population. Despite the small sample size and limited generalizability, missing data and inconsistencies were systematically addressed through data cleaning and normalization to maintain analytical reliability. The results suggest that K-means clustering can serve as an effective decision-support tool for public health planning, particularly for Non-Communicable Disease (NCD) prevention and diabetes management at the local level.

Keywords


3Es–2Ss Health Literacy; Diabetes Risk Classification; Diabetes Risk Populations; Health Behaviors; NCD prevention

Full Text:

PDF

References


H. Sun et al., “IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045,” Diabetes Research and Clinical Practice, vol. 183, p. 109119, Jan. 2022, doi: 10.1016/j.diabres.2021.109119.

E. Ahlqvist et al., “Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables,” The Lancet Diabetes & Endocrinology, vol. 6, no. 5, pp. 361–369, May 2018, doi: 10.1016/S2213-8587(18)30051-2.

P. Nuankaew, A. Kadkasame, K. Sawasit, P. Nasa-Ngium, T. Sararat, and W. S. Nuankaew, “Improving the Prediction Model of Food Consumption Behavior Analytics of Diabetic Patients in Northern Thailand Using Data Mining Techniques,” in Lect. Notes Networks Syst., Iglesias A., Shin J., Patel B., and Joshi A., Eds., Springer Science and Business Media Deutschland GmbH, 2025, pp. 25–40. doi: 10.1007/978-981-96-1741-8_3.

P. Nuankaew, A. Kadkasame, K. Sawasit, P. Nasa-Ngium, T. Sararat, and W. S. Nuankaew, “Applied Health Informatics for Diabetes Risk Prediction from Food Consumption Behavior of Rural Communities in Northern Thailand with Data Analytics,” in Lect. Notes Electr. Eng., Thampi S.M., Chaudhary V., Pathan A.-S.K., Ching Li K., and Krishnaswamy D., Eds., Springer Science and Business Media Deutschland GmbH, 2025, pp. 285–302. doi: 10.1007/978-981-97-4711-5_20.

N. Mayod, “Health literacy and health behaviors 3E 2S of Medical Profession,” Eur J Public Health, vol. 33, no. Supplement_2, p. ckad160.781, Oct. 2023, doi: 10.1093/eurpub/ckad160.781.

F. Al Sayah, S. R. Majumdar, B. Williams, S. Robertson, and J. A. Johnson, “Health Literacy and Health Outcomes in Diabetes: A Systematic Review,” J GEN INTERN MED, vol. 28, no. 3, pp. 444–452, Mar. 2013, doi: 10.1007/s11606-012-2241-z.

N. Ubolnuar, N. Luangpon, K. Pitchayadejanant, and S. Kiatkulanusorn, “Psychosocial and Physical Predictors of Stress in University Students during the COVID-19 Pandemic: An Observational Study,” Healthcare (Basel), vol. 10, no. 5, p. 786, Apr. 2022, doi: 10.3390/healthcare10050786.

P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65, Nov. 1987, doi: 10.1016/0377-0427(87)90125-7.

M. Bennasar-Veny et al., “Cluster Analysis of Health-Related Lifestyles in University Students,” International Journal of Environmental Research and Public Health, vol. 17, no. 5, p. 1776, Jan. 2020, doi: 10.3390/ijerph17051776.

D. H. Christensen et al., “Type 2 diabetes classification: a data-driven cluster study of the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) cohort,” BMJ Open Diabetes Res Care, vol. 10, no. 2, p. e002731, Apr. 2022, doi: 10.1136/bmjdrc-2021-002731.

B. Taurbekova et al., “Cluster Analysis in Diabetes Research: A Systematic Review Enhanced by a Cross-Sectional Study,” Journal of Clinical Medicine, vol. 14, no. 10, p. 3588, Jan. 2025, doi: 10.3390/jcm14103588.

M. A. Syakur, B. K. Khotimah, E. M. S. Rochman, and B. D. Satoto, “Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 336, no. 1, p. 012017, Apr. 2018, doi: 10.1088/1757-899X/336/1/012017.

M. Kalandarova et al., “Association Between Dietary Habits and Type 2 Diabetes Mellitus in Thai Adults: A Case-Control Study,” Diabetes Metab Syndr Obes, vol. 17, pp. 1143–1155, Mar. 2024, doi: 10.2147/DMSO.S445015.

L. M. Curtis et al., “Effectiveness of a health literacy intervention to improve diabetes outcomes in rural family medicine clinics: a randomized pragmatic trial,” Health Literacy and Communication Open, vol. 2, no. 1, p. 2382133, Dec. 2024, doi: 10.1080/28355245.2024.2382133.

A. Nagori, N. Keshvani, L. Patel, R. Dhruve, and A. Sumarsono, “Electronic health Literacy gaps among adults with diabetes in the United States: Role of socioeconomic and demographic factors,” Prev Med Rep, vol. 47, p. 102895, Nov. 2024, doi: 10.1016/j.pmedr.2024.102895.

M. A. Mizani et al., “Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals,” BMJ Open Diabetes Res Care, vol. 12, no. 3, p. e004191, June 2024, doi: 10.1136/bmjdrc-2024-004191.

A. M. W. Lim, E. U. Lim, P.-L. Chen, and C. S. J. Fann, “Unsupervised clustering identified clinically relevant metabolic syndrome endotypes in UK and Taiwan Biobanks,” iScience, vol. 27, no. 7, July 2024, doi: 10.1016/j.isci.2024.109815.

R. Todeschini, D. Ballabio, V. Termopoli, and V. Consonni, “Extended multivariate comparison of 68 cluster validity indices. A review,” 2024, doi: 10.1016/j.chemolab.2024.105117.

B. A. Hassan, N. B. Tayfor, A. A. Hassan, A. M. Ahmed, T. A. Rashid, and N. N. Abdalla, “From A-to-Z review of clustering validation indices,” Neurocomput., vol. 601, no. C, Oct. 2024, doi: 10.1016/j.neucom.2024.128198.

J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, vol. 5.1, University of California Press, 1967, pp. 281–298. Accessed: Aug. 21, 2025. [Online]. Available: https://projecteuclid.org/ebooks/berkeley-symposium-on-mathematical-statistics-and-probability/Proceedings-of-the-Fifth-Berkeley-Symposium-on-Mathematical-Statistics-and/chapter/Some-methods-for-classification-and-analysis-of-multivariate-observations/bsmsp/1200512992

T. Hastie, R. Tibshirani, and J. Friedman, “Unsupervised Learning,” in The Elements of Statistical Learning: Data Mining, Inference, and Prediction, T. Hastie, R. Tibshirani, and J. Friedman, Eds., New York, NY: Springer, 2009, pp. 485–585. doi: 10.1007/978-0-387-84858-7_14.

F. Al Sayah, S. R. Majumdar, B. Williams, S. Robertson, and J. A. Johnson, “Health literacy and health outcomes in diabetes: a systematic review,” J Gen Intern Med, vol. 28, no. 3, pp. 444–452, Mar. 2013, doi: 10.1007/s11606-012-2241-z.

M. T. Svendsen et al., “Associations of health literacy with socioeconomic position, health risk behavior, and health status: a large national population-based survey among Danish adults,” BMC Public Health, vol. 20, no. 1, p. 565, Apr. 2020, doi: 10.1186/s12889-020-08498-8.

S. S. Coughlin, M. Vernon, C. Hatzigeorgiou, and V. George, “Health Literacy, Social Determinants of Health, and Disease Prevention and Control,” J Environ Health Sci, vol. 6, no. 1, p. 3061, 2020.

L. Marciano, A.-L. Camerini, and P. J. Schulz, “The Role of Health Literacy in Diabetes Knowledge, Self-Care, and Glycemic Control: a Meta-analysis,” J Gen Intern Med, vol. 34, no. 6, pp. 1007–1017, June 2019, doi: 10.1007/s11606-019-04832-y.

U. Bhojani et al., “Constraints faced by urban poor in managing diabetes care: patients’ perspectives from South India,” Glob Health Action, vol. 6, p. 10.3402/gha.v6i0.22258, Oct. 2013, doi: 10.3402/gha.v6i0.22258.




DOI: https://doi.org/10.47738/jads.v7i2.1042

Refbacks

  • There are currently no refbacks.


Barcode

Journal of Applied Data Sciences

ISSN:2723-6471 (Online)
Publisher:Bright Publisher
Website:http://bright-journal.org/JADS
Email:taqwa@amikompurwokerto.ac.id (principal contact)
  support@bright-journal.org (technical issues)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0