Statistical analysis of factors affecting the incidence of diabetes mellitus
DOI:
https://doi.org/10.17721/1812-5409.2026/1.31Keywords:
time series, diabetes mellitus, forecasting and modeling, k-NN, Random Forest, logistic regression, algorithm accuracyAbstract
The seriousness of the problem of diabetes mellitus is explained by the scale of its prevalence. In 2021, approximately 537 million people worldwide were living with diabetes, which accounts for about 6% of the global population. Therefore, the study and analysis of data on the dynamics of diabetes incidence are highly relevant, both for forecasting future trends and for supporting effective government regulation and prevention of the disease’s spread.
The aim of this article is to conduct a statistical analysis of diabetes prevalence, to develop models of its dynamics for the city of Kyiv, and to analyze the influence of factors among women, such as the number of pregnancies, blood glucose levels, blood pressure, and body mass index (BMI), on the development of diabetes. Based on these variables, classification methods are applied to build predictive models using the R programming language.
Pages of the article in the issue: 237 - 241
Language of the article: English
References
Aguilera-Venegas, G., López-Molina, A., Rojo-Martínez, G., & Galán-García, J. L. (2023). Comparing and tuning machine learning algorithms to predict type 2 diabetes mellitus. Journal of Computational and Applied Mathematics, 427, 115115. https://doi.org/10.1016/j.cam.2023.115115
IDF. (2024). The the diabetes atlas. https://diabetesatlas.com.ua/ua/kyiv.
Iftikhar, K., Javaid, N., Ahmed, I., & Alrajeh, N. (2025). A novel explainable deep learning framework for accurate diabetes mellitus prediction. Applied Sciences, 15(16), 9162. https://doi.org/10.3390/app15169162
Kangra, K., & Singh, J. (2023). Comparative analysis of predictive machine learning algorithms for diabetes mellitus. Bulletin of Electrical Engineering and Informatics, 12(3), 1728–1737. https://doi.org/10.11591/eei.v12i3.4412
Livinska, H., Skrypnyk, D., & Galán-García, J. L. (2024). Comparative study of machine learning algorithms for diabetes detection using binary data. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics, 78(1), 119–127. https://doi.org/10.17721/1812-5409.2024/1.23
Olisah, C. C., Smith, L., & Smith, M. (2022). Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective. Computer Methods and Programs in Biomedicine, 220, 106773. https://doi.org/10.1016/j.cmpb.2022.106773
Orlova, N., Tkachenko, O., Herasymiuk, K., Palamar, I., & Sereda, N. (2026). Epidemiological situation of type 2 diabetes mellitus in ukraine: challenges and priorities for public health management. Ukraine. Nation’s Health, 1, 28–37. https://doi.org/10.32782/2077-6594/2026.1/03
Smith, J., Everhart, J., Dickson, W., Knowler, W., & Johannes, R. (1988). Using the adap learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the symposium on computer applications and medical care (pp. 261–265). IEEE Computer Society Press. https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database
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