Polynomial approach modeling among diabetic patients associated with age in rural hilly population of Dehradun district, Uttarakhand
DOI:
https://doi.org/10.18203/2320-6012.ijrms20180615Keywords:
Cross Validity Prediction Power (CVPP), Distribution of Diabetic Patients, F-Test, Polynomial Model, Variance Explained (R2)Abstract
Background: Diabetes mellitus is a form of infections that includes issues with the hormone insulin. It is described by constant rise of blood glucose level surprising ordinary esteem. In this paper, an exertion has been made to fit scientific model to diabetic patients and additionally its total dispersion for both genders related with time of rural population from Dehradun district, Uttarakhand.
Methods: For this reason, the information have been taken from field overview in rural hilly population of Dehradun district. In this investigation, an endeavor has been given to demonstrate that the polynomial model is attempted to fit to the conveyance of diabetic patients related with age and also its cumulative distribution.
Results: The fitted model provides statistically significant values with R2=0.9997 and ρcv2= 0.994857. This is the polynomial of degree four, i.e. bi-quadratic polynomial model. The polynomial model is assumed for the cumulative distribution of diabetic patients associated with age and the fitted model provides statistically significant values providing R2= 0.99998 and ρcv2= 0.999983 and shrink-age coefficient=0.00001414. This is the polynomial of degree three, i.e. cubic polynomial model. From this statistic we see that the fitted models are highly cross-validated, and their shrinkages are 0.004842857 and 0.00001414 for the models (1) and (2) respectively.
Conclusions: It is discovered that the distribution of diabetic patients for both genders related with age takes after bi-quadratic polynomial model. In addition, it is found that cumulative distribution of diabetic patients takes as cubic polynomial model. Cross validity prediction power is utilized to the fitted model to verify the stability of the model in this study.
References
Estrada GC, Kirchsteiger H, del Re L, Renard E. Model based validation of meal inputs in diabetes therapy. IFAC Proceedings Volumes. 200;42(10):239-44.
Fernandez M, Villasana M, Streja D. Glucose dynamics in Type I diabetes: Insights from the classic and linear minimal models. Computers in biology and medicine. 2007;37(5):611-27.
Finan DA, Jørgensen JB, Poulsen NK, Madsen H. Robust model identification applied to type 1 diabetes. InAmerican Control Conference (ACC), 2010 2010 Jun 30 (pp. 2021-2026). IEEE.
Islam MR. Modeling of diabetic patients associated with age: Polynomial model approach. Inter J Statistics Applications. 2011;1(1):1-5.
Bagust A, Hopkinson PK, Maier W, Currie CJ. An economic model of the long-term care burden of type II diabetes. Diabetologia. 2001;44:2140-55.
Stevens RJ, Kothari V, Adler AI, Stratton IM, Holman RR, (UKPDS Group). The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). Clinical Sci. 2001;101:671-9.
Boutayeb A, Chetouani A, Achouyab K, Twizell EH. A non-linear population model of diabetes mellitus. J App Mathem computing. 2006;21:127-139.
Islam R. Mathematical modeling of age specific marital fertility rates of Bangladesh. Res J Mathematics Statictics. 2009;1(1):19-22.
Briggs A, Sculpher M. An introduction to Markov modelling for economic evaluation. Pharmacoeconomics. 1998;13(4):397-409.
Islam MR, Islam MN, Ali MK, Mondal MN. Indirect estimation and mathematical modeling of some demographic parameters of Bangladesh. Oriental Anthropologist. 2005;5(2):163-71.
Stevens J. Applied Multivariate Statistics for the Social Sciences, 3rd Edition, Lawrence Erlbaum Associates, Inc., Publishers, New Jersey;1996.
Breton M, Kovatchev B. Analysis, modeling, and simulation of the accuracy of continuous glucose sensors. J Diabetes Sci Technol. 2008;2(5):853-62.
Islam MR, Islam MN, Ali MA, Mostofa MG. Construction of male life table from female widowed information of Bangladesh. Inter J Statis Sci. 2003;2:69-82.
Gupta SC, Kapoor VK. Fundamentals of Mathematical Statistics, Ninth Extensively. Revised Edition, Sultan Chand & Sons, Educational Publishers, New Delhi;1997.
Loney SL. An Elementary Treatise on the Dynamics of a Particle and of Rigid Bodies. Cambridge University Press, London;1990.
Gujarati DN. Basic Econometrics, Third Edition, McGraw Hill, Inc., New York;1998.
