Statistical Models for Predicting the Expected Time of Diabetes Mellitus Using Exponentiated Gamma Distribution

Ezhilvanan Mani¹ , Kannadasan Karuppaiah² , Vinoth Raman³

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.4 , pp.30-34, Aug-2023

Online published on Aug 31, 2023

Copyright © Ezhilvanan Mani, Kannadasan Karuppaiah, Vinoth Raman . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In order to produce effective medical diagnoses, knowledge discovery from medical datasets is critical. With the rising prevalence of diabetes, which now affects about 346 million people worldwide and more than one-third of whom go undiagnosed in the early stages, there is an urgent need to help medical decision-making. Diabetes mellitus is a chronic disease that has become a major public health issue around the world. The model is used to calculate the diabetes mellitus anticipated time. The analytical findings are supported by numerical examples.

Key-Words / Index Term :
Non Communicable Diseases, Diabetes mellitus, Expected Time, Threshold and Exponentiated Gamma distribution.

References :
[1]. A.I.Shawky and R. A. Bakoban. “Bayesian and Non-Bayesian Estimations on the Exponentiated Gamma Distribution”, Applied Mathematical Sciences, Vol.2, (51), pp. 2521-2530, 2008.
[2]. A.I.Shawky and R.A.Bakoban. “Order Statistics from Exponentiated Gamma Distribution and Associated Inference”, Int. J. Contemp. Math. Sciences, Vol.4, (2), pp. 71-91, 2009.
[3]. American Diabetes Association. “Diagnosis and classification of diabetes mellitus”. Diabetes Care 35 (Suppl. 1), S64-S71. 2012.
[4]. Chang C. “Progress in diabetes prevention and health education”. Chin J Health Educ. Vol.10, pp.790-792, 2008.
[5]. Cox, M. E., and Edelman, D. “Tests for screening and diagnosis of type 2 diabetes”. Clin. Diabetes. Vol. 27, pp.132-138, 2009.
[6]. Global report on diabetes. Geneva: World Health Organization; 2016.
[7]. Gupta, R.C., Gupta, P.L. and Gupta, R.D. “Modeling failure time data by Lehman alternatives”. Communications in Statistics-Theory and Methods, Vol. 27, (4), 1998.
[8]. Iancu, I., Mota, M., and Iancu, E. “Method for the analyzing of blood glucose dynamics in diabetes mellitus patients,” in Proceedings of the 2008 IEEE International Conference on Automation, Quality and Testing, Robotics, 2008.
[9]. IDF Diabetes Atlas. 8th Edition. Brussels: International Diabetes Federation, 2017
[10]. Krasteva, A., Panov, V., Krasteva, A., Kisselova, A., and Krastev, Z.” Oral cavity and systemic diseases Diabetes Mellitus”. Biotechnol. Equip. Vol.25, pp.2183-2186, 2011.
[11]. Leslie RD, Palmer J, Schloot NC, Lernmark A. “Diabetes at the crossroads: relevance of disease classification to pathophysiology and treatment”. Diabetologia. Vol.59, pp.13–20, 2016.
[12]. Lihua Z, Li Q, Lihua W, Li R, Yu Z, Jumei X, Jingwei L. “Study on the effect of experiential health education on diabetic complications”. Chin J Nurs. Vol.53, (1), pp.36-40, 2018.
[13]. Lonappan, A., Bindu, G., Thomas, V., Jacob, J., Rajasekaran, C., and Mathew, K. T. “Diagnosis of diabetes mellitus using microwaves”. J. Electromagnet. Wave. Vol.21. pp.1393-1401, 2007.
[14]. Quan Zou, Kaiyang Qu, Yamei Luo, Dehui Yin, Ying Ju and Hua Tang. Predicting Diabetes Mellitus with Machine Learning Techniques. Frontiers in Genetics, Vol.9, (6), pp.1-10, 2018.
[15]. Robertson, G., Lehmann, E. D., Sandham, W., and Hamilton, D. “Blood glucose prediction using artificial neural networks trained with the AIDA diabetes simulator: a proof-of-concept pilot study”. J. Electr. Comput. Eng. 2011.
[16]. Van Dieren S, Beulens JW, van der Schouw YT, Grobbee DE, Neal B. “The global burden of diabetes and its complications: an emerging pandemic”. Eur J Cardiovasc Prevention Rehab. Vol.17, (Suppl 1):S3-8, 2010.
[17]. Yanan Z. “Research on risk prediction of type 2 diabetes mellitus based on data mining technology”. Master Thesis. Yanshan University. 2017.