Adaptive Neuro-Fuzzy System to Determine the Blood Glucose Level of Diabetic

Nata’ala, Auwal; Muazu, Hamman Dikko; Goni, Ibrahim; Jingi, Abdullahi Mohammed

doi:10.11648/j.mcs.20190403.11

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…), properties of exogenous sources of glucose and insulin intake, etc. As basic mathematical representations, analytical schemes based on linear autoregressive mappings are used [8,9,10] and neural network mathematical constructions [11,12]. 2).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Neural network empirical models are also generally quite efficiently tuned to specific patients and make it possible to predict future glucose levels [11,12] in his blood based on several previous values and / or some currently existing factors regulating carbohydrate metabolism. At the same time, it should be borne in mind that neural network models, before using them, require a training procedure, i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

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Modification of the Minimal Bergman Model of the "Insulin-Glucose" System and Its Implementation in Matlab/Simulink

Belov¹,

Procofiev²,

Komandresova

et al. 2021

ETR

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The article discusses a modification of Bergman's minimal mathematical model of the "insulin-glucose" system, which allows simulating controlled exogenous sources of glucose and insulin into the patient's blood on the model and investigating the dynamics of changes in their concentrations in normal conditions, in type I DM and type II DM. A modeling scheme is presented in graphic notations of the MatLab / Simulink computer mathematics system and a number of computational experiments on it are described to determine the type of glycemic profiles of glucose and insulin concentration in the patient's blood in the noted situations. The fundamental possibility of using model mappings in the MatLab/Simulink environment for the study and tuning of the loop for automatic regulation of the "insulin-glucose" balance in the patient's blood using a controlled insulin pump is demonstrated. It was also found that the modified minimal model can be customized for a specific patient with diabetes, which makes it possible to use it to solve the problems of individual prediction of the development of a diabetic disease in a specific patient. In addition, the described model makes it possible to recreate and virtually investigate various conditions and cases on it that affect the dynamics of insulin and glucose concentrations in the patient's blood, for example, when he performs physically stressed activities, in the presence of the effects of “aging” of insulin-producing cells in the pancreas. iron, etc.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Modification of the Minimal Bergman Model of the "Insulin-Glucose" System and Its Implementation in Matlab/Simulink

Belov¹,

Procofiev²,

Komandresova

et al. 2021

ETR

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“…At present, it is being deployed in several medical prognosis and treatment procedures. For instance, ANFIS technique is used to: determine the blood sugar levels of a diabetic person 8 predict the duration of stay in ICU at the time of cardiac arrest 9 assure security in web-based neuroscience applications; 10 predict chronic kidney disease 11 and assess the risk in software projects which find their application in healthcare scenario. 12 The empirical study undertaken in this research endeavour also found that the proposed ANFIS provides a better estimation of the security risks at early developmental phases.…”

Section: Introductionmentioning

confidence: 99%

<p>Security Risk Assessment of Healthcare Web Application Through Adaptive Neuro-Fuzzy Inference System: A Design Perspective</p>

Kaur¹,

Khan²,

Abushark³

et al. 2020

RMHP

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Introduction:The imperative need for ensuring optimal security of healthcare web applications cannot be overstated. Security practitioners are consistently working at improvising on techniques to maximise security along with the longevity of healthcare web applications. In this league, it has been observed that assessment of security risks through soft computing techniques during the development of web application can enhance the security of healthcare web applications to a great extent. Methods: This study proposes the identification of security risks and their assessment during the development of the web application through adaptive neuro-fuzzy inference system (ANFIS). In this article, firstly, the security risk factors involved during healthcare web application development have been identified. Thereafter, these security risks have been evaluated by using the ANFIS technique. This research also proposes a fuzzy regression model. Results:The results have been compared with those of ANFIS, and the ANFIS model is found to be more acceptable for the estimation of security risks during the healthcare web application development. Conclusion:The proposed approach can be applied by the healthcare web application developers and experts to avoid the security risk factors during healthcare web application development for enhancing the healthcare data security.

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