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J. U. Poulsen, A. Avogaro, F. Chauchard, C. Cobelli, R. Johansson, L. Nita, M. Pogose, L. Del Re, E. Renard, S. Sampath, F. Saudek, M. Skillen, and J. Soendergaard, “A diabetes management system empowering patients to reach optimised glucose control: from monitor to advisor.,” in Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2010, vol. 2010, pp. 5270-1.

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Article

Fuzzy Inference Model for Type 2 Diabetes Management: a Tool for Regimen Alterations

1School of Computing and Mathematical Science, Liverpool John Moores University, Liverpool, United Kingdom

2Diabetes and Endocrinology department, Royal Liverpool and Broadgreen University Hospitals, Liverpool, United Kingdom

3Information Management and Technology department, Royal Liverpool and Broadgreen University Hospitals, Liverpool, United Kingdom


Journal of Computer Sciences and Applications. 2015, Vol. 3 No. 3A, 40-45
DOI: 10.12691/jcsa-3-3A-5
Copyright © 2015 Science and Education Publishing

Cite this paper:
Nonso Nnamoko, Farath Arshad, David England, Jiten Vora, James Norman. Fuzzy Inference Model for Type 2 Diabetes Management: a Tool for Regimen Alterations. Journal of Computer Sciences and Applications. 2015; 3(3A):40-45. doi: 10.12691/jcsa-3-3A-5.

Correspondence to: Nonso  Nnamoko, School of Computing and Mathematical Science, Liverpool John Moores University, Liverpool, United Kingdom. Email: N.A.Nnamoko@2011.ljmu.ac.uk

Abstract

This paper aims to demonstrate the utility of fuzzy set theory in the design process of a diabetes management system that enables patients to make short term alterations (particularly lifestyle) to their overall regimen as required. The model is a Mamdani Fuzzy Inference System (FIS) configured through domain specific information from experts and recognised diabetes management algorithms. The FIS takes a multi-input multi-output (MIMO) design approach with seven inputs variables (age, gender, weight, height, blood glucose (BG), exercise and diet) and three outputs (glycatedhaemoglobin (A1c), exercise and diet level assessments). Goodness of fit test was conducted based on Mean Square Error (MSE), Normalised Mean Square Error (NMSE) and Normalised Root Mean Square Error (NRMSE) between observed/advised and predicted output values. Overall MSE of 0.0899 shows good fit. For each of the output pairs (A1c, exercise and diet), NRMSE (0.7387, 0.7881 and 0.3716) and NMSE (0.9317, 0.9551 and 0.6051) shows good fit for A1c and exercise, but poor fit for diet. Intelligent models of this sort can help simplify management information for diabetes patients, reduce routine workload for clinicians and allow them to focus more on critical issues. Fully developed, this system can be used to build a database of diabetes management cases that includes daily life event information, ultimately leading to automated care for patients through technology.

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