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 The Prevalence of Diabetes in the Republic of Kazakhstan Based on Regression Analysis Methods



   Volume 5
A. Mukasheva, N. Saparkhojayev, Z. Akanov, A. Algazieva

Published online: 08 March 2019

Article Views: 25

Abstract

In this research paper, experimental studies of regression analysis methods for predicting diabetes mellitus patients for 2019 in the Republic of Kazakhstan were conducted. Linear, polynomial, and exponential regressions methods were considered, after which appropriate graphs were built. According to these results, the growth of development of patients with diabetes mellitus will not decrease. This is another confirmation that researchers need to apply new modern information technologies based on machine learning and artificial intelligence to struggle with the growth of this disease. Based on the results obtained, it can be concluded that early detection of the disease and preventive measures to increase risk awareness and complications of diabetes in the population is an important vector in preventing diabetes.

Reference

  1. World Health Organization. (2018) Diabetes. [Online]. Available: https://bit.ly/36tvQMf
  2. World Health Organization. (2016) Global report on diabetes. [Online]. Available: https://bit.ly/2RMZNCT
  3. International Diabetes Federation. (n.d.) Epidemiology and research. [Online]. Available: https://bit.ly/2t6Chq3
  4. T. Sungkhapong, P. Prommete, N. Martkoksoong, and B. Kittichottipanich, “The health behaviors’ modification for controlling and prevention of diabetes mellitus by using promise model at premruthai pravate community bangkok,” Journal of Advances in Health and Medical Sciences, vol. 2,no. 3, pp. 97–101, 2016. doi: https://doi.org/10.20474/jahms2.3.3
  5. Y. H. Tang, S. M. Pang, M. F. Chan, G. S. Yeung, and V. T. Yeung, “Health literacy, complication awareness, and diabetic control in patients with type 2 diabetes mellitus,” Journal of Advanced Nursing, vol. 62, no. 1, pp. 74–83, 2008. doi:https://doi.org/10.1111/j.1365-2648.2007.04526.x
  6. D. Premkumar, “Awareness of diabetes mellitus and its complications among students in a Malaysian university,” Journal of Medicine, Radiology, Pathology and Surgery, vol. 5, no. 4, pp. 1–4, 2018. doi: https://doi.org/10.15713/ins.jmrps.134
  7. N. Murugesan, C. Snehalatha, R. Shobhana, G. Roglic, and A. Ramachandran, “Awareness about diabetes and its complications in the general and diabetic population in a city in Southern India,” Diabetes Research and Clinical Practice, vol. 77, no. 3, pp. 433–437, 2007. doi: https://doi.org/10.1016/j.diabres.2007.01.004
  8. C. M. J. Nazar, M. M. Bojerenu, M. Safdar, and J. Marwat, “Effectiveness of diabetes education and awareness of diabetes mellitus in combating diabetes in the United Kigdom; a literature review,” Journal of Nephropharmacology, vol. 5, no. 2, pp.110–115, 2016.
  9.  World Health Organization. (2016) Diabetes  country profiles. [Online]. Available: https://bit.ly/2YJwzpM
  10.  Kazakhstan Pharmaceutical Bulletin. (n.d.) Zhanay Akanov: The situation is getting out of control. [Online]. Available: https://bit.ly/2RPxQdA
  11.  J. E. Shaw, R. A. Sicree, and P. Z. Zimmet, “Global estimates of the prevalence of diabetes for 2010 and 2030,” Diabetes Research and Clinical Practice, vol. 87, no. 1, pp. 4–14, 2010. doi: https://doi.org/10.1016/j.diabres.2009.10.007
  12. N. Saparkhojayev, A. Mukasheva, and P. Saparkhojayev, “The development of information system of formation and use of information resources for evaluation of parameters and evaluation of recommendations based on big data technology tools:Work with Mongo DB,” in International Conference on Cyber Security and Computer Science (ICONCS’18), Safranbolu, Turkey, 2018.
  13.  N. Saparkhojayev, A. Mukasheva, and P. Saparkhojayev, “The concept of monetization of IoT-based project: Case of medical system in Kazakhstan,”in The 15th International Scientific  Conference Information Technologies and Management, Riga, Latvia, 2017.
  14. N. Saparkhojayev and A. Mukasheva, “Introduction to BigData technology for diagnosis of diabetes,” The 16th International Scientific Conference Information Technologies and Management, Riga, Latvia, 2018.
  15.  N. Saparkhojayev, A. Mukasheva, B. Tussupova,  and I. Zimin, “Development of the information system based on BigData technology to support endocrinologist-doctors for diagnosis and treatment of diabetes in Kazakhstan,” International Smartcity Symposium, Putrajaya, Malaysia, 2018.
  16. K. Prabsangob, “Relationships of health literacy diabetes knowledge and social support to self-care behavior among type 2 diabetic patients,” International Journal of Health and Medical Sciences, vol. 2, no. 3, pp. 68–72, 2016. doi: https://doi.org/10.20469/ijhms.2.30005-3
  17. R. R. Isnanto, D. Eridani, and S. S. Y. W. Simbolon, “Expert system for diabetes mellitus detection and handling using certainty factor on androidbased mobile device,” International Journal of Health and Medical Sciences, vol. 4, no. 2, pp. 28– 39, 2018. doi: https://dx.doi.org/10.20469/ijhms.40001-2
  18.  R. Dobson. (2018) Time to halt worldwide spread of diabetes. [Online]. Available: https://bit.ly/2Phq3n5
  19.  S. Tavernise. (2015) Global diabetes rates are rising as obesity spreads. [Online]. Available: https://nyti.ms/35eqPa6
  20. M. Rowe. (2017) Diabetes: The world at risk. [Online]. Available: https://bit.ly/36qirEC
  21.  N. Emery. (2012) The global diabetes epidemic, brought to you by global development. [Online]. Available: https://bit.ly/2PkasU6
  22.  C. Hu and W. Jia, “Diabetes in China: Epidemiology and genetic risk factors and their clinical utility in personalized medication,” Diabetes, vol. 67, no. 1, pp. 3–11, 2018. doi: https://doi.org/10.2337/dbi17-0013
  23.  A. Schneider, G. Hommel, and M. Blettner, “Linear regression analysis,” Deutsches Ärzteblatt International, vol. 107, no. 44, pp. 776–782, 2010.
  24.  C. Combes, F. Kadri, and S. Chaabane, “Predicting hospital length of stay using regression models: application to the emergency department,” in 10th Francophone Conference on Modeling, Optimization and Simulation, Nancy, France, 2014.
  25.  D. Gregori, M. Petrinco, S. Bo, A. Desideri, F. Merletti, and E. Pagano, “Regression models for analyzing costs and their determinants in health care:An introductory review,” International Journal for Quality in Health Care, vol. 23, no. 3, pp. 331–341, 2011. doi: https://doi.org/10.1093/intqhc/mzr010
  26.  G. H. Skrepnek, “Regression methods in the empiric analysis of health care data,” Journal of Managed Care Pharmacy, vol. 11, no. 3, pp. 240–251, 2005. doi: https://doi.org/10.18553/jmcp.2005.11.3.240
  27.  A. Kogure, “Predicting health care costs by two part model with sparse regularization,” in The World Risk and Insurance Economics Congress, Munich, Germany, 2015.
  28.  M. Griswold, G. Parmigiani, A. Potosky, and J. Lipscomb, “Analyzing health care costs: A comparison of statistical methods motivated by medicare colorectal cancer charges,” Biostatistics, vol. 1, no. 1, pp. 1–23, 2004.
  29.  A. S. Malehi, F. Pourmotahari, and K. A. Angali, “Statistical models for the analysis of skewed healthcare cost data: A simulation study,” Health Economics Review, vol. 5, no. 1, pp. 1–16, 2015. doi: http://dx.doi.org/10.1186/s13561-015-0045-7
  30.  Republic of Kazakhstan. (n.d.) Kazakhstan society for the study of diabetes. [Online]. Available:https://www.kssd.site/
  31.  T. J. Cleophas and A. H. Zwinderman, Regression Analysis in Medical Research: For Starters and 2nd Levelers. Cham, Switzerland: Springer, 2018.
  32. Alexey. (n.d) 3 ways to calculate polynomial in excel. [Online]. Available: https://bit.ly/2LQbNzK
  33.  D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to linear regression analysis. Hoboken, NJ: John Wiley & Sons, 2012.
  34. Excel2.ru. (n.d) Mncs: Exponentioal dependence in excel. [Online]. Available: https://bit.ly/2sjOnfl
  35.  Centers for Disease Control and Prevention. (2017) Statistics about diabetes. [Online]. Available: https://bit.ly/2srwqLz

To Cite this article

S. Mukasheva, N. Saparkhojayev, Z. Akanov, and A. Algazieva, “The prevalence of diabetes in the Republic of Kazakhstan based on regression analysis methods,” International Journal of Health and Medical Sciences, vol. 5, no. 1, pp. 8-16. doi: https://dx.doi.org/10.20469/ijhms.5.30002-1



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