Development of a fuzzy expert system for the control of glycemia in type 1 diabetic patients

Nobile, Leonardo; Cosenza, Biagio; Amato, Marco; Guarnotta, Valentina; Giordano, Carla; Galluzzo, Aldo; Galluzzo, M.

doi:10.1016/b978-0-444-54298-4.50092-1

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…This system shows promising results in detecting the number of insulin units to be provided to a patient to keep the blood glucose level near to the homeostatic condition after a meal. 12 As a new artificial intelligence method to classify BP, a neuro-fuzzy hybrid model (NFHM) is proposed. This model helped in monitoring the BP of patients for 24 h. To classify the trends, fuzzy system is used that has rules, and by genetic algorithm, these rules are optimized to obtain the best possible rules for the classifier.…”

Section: Related Workmentioning

confidence: 99%

A proposed health monitoring system using fuzzy inference system

Ghosh

Roy

Merdji

2020

Proc Inst Mech Eng H

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Due to the busy schedule of every human being in today’s world, consciousness towards one’s health has become quite alarming. A person suffering from any chronic disease needs a gradual, regular and close monitoring to recover from the disease or to be under control. Because of heavy work pressure, anxiety, change of weather and location or due to some other causes, the effect of the diseases can turn up into an appalling state. Two vital aspects of human diseases are blood pressure (hypertension) and blood sugar imbalance. Hypertension is one of the complications of prolonged untreated diabetes. Other organs like kidney, eye and peripheral nerves are also involved. The various gradations of hypertension and diabetes are required to understand for the progression of the disease and to make plan for the treatment. So these two aspects are considered in this article. The idea is to develop a logic, which could be incorporated in a pocket friendly device in future that would generate an alarm whenever there is imbalance in blood sugar or blood pressure levels. The concept of the fuzzy inference rule and first-order logic is implemented to develop this study.

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Section: Related Workmentioning

confidence: 99%

A proposed health monitoring system using fuzzy inference system

Ghosh

Roy

Merdji

2020

Proc Inst Mech Eng H

View full text Add to dashboard Cite

show abstract

“…This is a serious advantage with respect to the state-of-the-art on the representation and learning in multi-level hierarchical fuzzy inference systems [35][36][37][38][39][40][41] as it offers a transparent algorithmic complexity representation.…”

Section: Introductionmentioning

confidence: 96%

“…Some of the well accepted modelling that deal with rule bases in fuzzy models concern the sparse hierarchical rule bases [23], the fuzzy rule interpolation [24][25][26][27][28][29], the algebraic and the mathematical analysis of the properties of rule bases [30,31], the manipulation of operators [32], the development of evolving fuzzy systems [33] or the symbolic representation of data in terms of fuzzy signatures [34]. The current approaches to the representation and learning of multi-level hierarchical fuzzy inference systems include the observer-based adaptive controllers developed from hierarchical fuzzy neural networks [35], the adaptive fuzzy controllers based on variable structure algorithms [36], fuzzy expert systems [37], the analysis of interpretability measures [38], the identification of linguistic fuzzy models [39], the modelling in the framework of function approximation problems [40], or the use of linguistic preferences and incomplete information in modelling based on multi-level hierarchical fuzzy inference systems [41].…”

Section: Introductionmentioning

confidence: 99%