ArticleDiabetes therapy : research, treatment and education of diabetes and related disorders2019
Forecasting the Prevalence of Diabetes Mellitus Using Econometric Models.
Article in Diabetes therapy : research, treatment and education of diabetes and related disorders, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- ECG Forecasting System Based on Long Short-Term Memory.Bioengineering (Basel, Switzerland) · 2024Article
- [Association between treatment outcome and age in tuberculosis and diabetes patients: a population analysis].Problemy endokrinologii · 2023Article
- The value of lost output and cost of illness of noncommunicable diseases in the Pacific.Health policy OPEN · 2022Article
- A New Way of Investigating the Relationship Between Fasting Blood Sugar Level and Drinking Glucose Solution.Frontiers in nutrition · 2022Article
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Authors and funding
5 authors.
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Abstract
introductionThe prevalence of diabetes in Kazakhstan has reached epidemic proportions, and this disease is becoming a major financial burden. In this research, regression analysis methods were employed to build models for predicting the number of diabetic patients in Kazakhstan in 2019, as this should aid the costing and policy-making performed by medical institutions and governmental offices regarding diabetes prevention and treatment strategies.
methodsA brief review of mathematical models that are potentially useful for the task of interest was performed, and the most suitable methods for building predictive models were selected. The chosen models were applied to explore the correlation between population growth and the number of patients with diabetes as well as the correlation between the increase in gross regional product and the growth in the number of patients with diabetes. Moreover, the relationship of population growth and gross domestic product with the growth in the number of patients with diabetes in Kazakhstan was determined. Our research made use of the scikit-learn library for the Python programming language and functions for regression analysis built into the Microsoft Excel software.
resultsThe predictive models indicated that the prevalence of diabetes in Kazakhstan will increase in 2019.
conclusionMathematical models were used to find patterns in a comprehensive statistical dataset on registered diabetes patients in Kazakhstan over the last 15 years, and these patterns were then used to build models that can accurately predict the prevalence of diabetes in Kazakhstan.
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