Using data mining methods for risk assessment and intervention planning in diabetic patients-An exploratory study
DOI:
https://doi.org/10.47203/IJCH.2024.v36i02.019Keywords:
Cluster analysis, Data mining, Diabetes Mellitus, Medical InformaticsAbstract
.
Downloads
References
World Health Organization. Diabetes. Accessed December 4, 2022. https://www.who.int/health-topics/diabetes
Guariguata L, Whiting DR, Hambleton I, et al. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract. 2014;103(2):137-149.
Bommer C, Heesemann E, Sagalova V, et al. The global economic burden of diabetes in adults aged 20–79 years: a cost-of-illness study. Lancet Diabetes Endocrinol. 2017;5(6):423-430.
Sarría-Santamera A, Orazumbekova B, Maulenkul T, et al. The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review. Int J Environ Res Public Health. 2020;17(24):9523.
Ahlqvist E, Storm P, Käräjämäki A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361-369.
Eby EL, Edwards A, Meadows E, et al. Evaluating the relationship between clinical and demographic characteristics of insulin-using people with diabetes and their health outcomes: a cluster analysis application. BMC Health Serv Res. 2021;21(1):669.
Wang X, Gao H, Xu H. Cluster Analysis of Unhealthy Lifestyles among Elderly Adults with Prediabetes: A Cross-Sectional Study in Rural China. Diabetes Ther Res Treat Educ Diabetes Relat Disord. 2019;10(5):1935-1948.
Nnoaham KE, Cann KF. Can cluster analyses of linked healthcare data identify unique population segments in a general practice-registered population? BMC Public Health. 2020;20(1):798.
Ogbuabor G, F. N U. Clustering Algorithm for a Healthcare Dataset Using Silhouette Score Value. Int J Comput Sci Inf Technol. 2018;10(2):27-37.
Robertson L, Vieira R, Butler J, et al. Identifying multimorbidity clusters in an unselected population of hospitalised patients. Sci Rep. 2022;12(1):5134.
Do CB, Batzoglou S. What is the expectation maximization algorithm? Nat Biotechnol. 2008;26(8):897-899.
Meila M, Heckerman D. An Experimental Comparison of Model-Based Clustering Methods. Mach. Learn. 2001;42: 9-29.
Wang T, Zhao Z, Wang G, et al. Age-related disparities in diabetes risk attributable to modifiable risk factor profiles in Chinese adults: a nationwide, population-based, cohort study. Lancet Healthy Longev. 2021;2(10):e618-e628.
Bahour N, Cortez B, Pan H, et al. Diabetes mellitus correlates with increased biological age as indicated by clinical biomarkers. GeroScience. 2022;44(1):415-427.
Nguyen QM, Xu JH, et al. Correlates of Age Onset of Type 2 Diabetes Among Relatively Young Black and White Adults in a Community. Diabetes Care. 2012;35(6):1341- 1346.
Zhang L, Yang H, Yang P. The Correlation between Type 2 DiabetesMellitus and Cardiovascular Disease Risk Factors in the Elderly. Appl Bionics Biomech. 2022;2022: e4154426.
Lee MK, Han K, Kwon HS. Age-specific diabetes risk by the number of metabolic syndrome components: a Korean nationwide cohort study. Diabetol Metab Syndr. 2019;11(1):112.
Uddin R, Lee EY, Khan SR, et al. Clustering of lifestyle risk factors for non- communicable diseases in 304,779 adolescents from 89 countries: A global perspective. Prev Med. 2020; 131:105955.
Lefèvre T, Rondet C, Parizot I, et al. Applying Multivariate Clustering Techniques to Health Data: The 4 Types of Healthcare Utilization in the Paris Metropolitan Area. Divaris K, ed. PLoS ONE. 2014;9(12):e115064.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Vanisree Ramanathan, Sharyu Mhamane, Jayesh Pawar, Nisha P. K., Ujjwal Kumar, Shailesh Tripathi, Keerti Pradhan, Sudip Bhattacharya
![Creative Commons License](http://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.