Hierarchical Takagi-Sugeno type fuzzy system for diabetes mellitus forecasting

Sharifi, Arash; Vosolipour, A.; Sh, Mahdi Aliyari; Teshnehlab, Mohammad

doi:10.1109/icmlc.2008.4620599

Cited by 21 publications

(9 citation statements)

References 15 publications

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“…The pancreas of a healthy human produces and stores insulin hormones and therefore enables the body to perform metabolic processes in which the sugar and other food converts into the energy that a human needs and regulates blood glucose level. However, the main cause of diabetes is still unknown, but roles of hereditary factors and environmental conditions have been proved to be a factor [3]. Diabetes can be divided into two types: the first is type-1 diabetes which is caused by destruction of beta cells in the pancreas by the immune system.…”

Section: Introductionmentioning

confidence: 99%

An intelligent approach for optimal regulation of blood glucose level

Beyki

Javan

Kambiz

et al. 2010

2010 17th Iranian Conference of Biomedical Engineering (ICBME)

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Diabetes mellitus, a situation resulting from the inability of the pancreas in controlling blood glucose level, can lead to serious long-term complications and death. In this paper, the model of type-1 diabetes mellitus has been studied considering the inputs of the system which are daily meals and the amount of insulin injected. With the help of particle swarm optimization algorithm and the use of proposed controller based on the Hammerstein model, an optimized value of injected insulin has been determined in order to control blood glucose level. Results obtained have been compared to results of the classic controller, and that demonstrates improvement in operation of the proposed controller's algorithm.

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Section: Introductionmentioning

confidence: 99%

An intelligent approach for optimal regulation of blood glucose level

Beyki

Javan

Kambiz

et al. 2010

2010 17th Iranian Conference of Biomedical Engineering (ICBME)

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“…some are improved by combining the features or classification results such as: boosting [24], owa operator and decision template [7,29], Majority voting [25], etc. The others are evolved from evolutionary algorithms such as: Neural networks [32], Genetic Programming [26], particle swarm optimization [33], artificial immune recognition systems [5], etc.…”

Section: Introductionmentioning

confidence: 99%

A neuro-fuzzy immune inspired classifier for task-oriented texts

Mahmoudi

Taghiyareh

Araabi

2013

Journal of Intelligent &Amp; Fuzzy Systems

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Increasing growth of task-oriented texts specifically in organizations, have become a catastrophe nowadays. To overcome this problem, potential classification methods are improved. This paper outlines the capability of neuro-fuzzy approach and artificial immune recognition systems to enhance task-oriented texts classification. Task-oriented texts stand for various kinds of texts which are organized to help the users with their different tasks such as: research, development, learning, justification, innovation and analysis. In this respect, seven major attributes with three nominal values of low, medium and high are considered to classify text into six task classes. To illustrate the capabilities of proposed approaches, Takagi-Sugeno as a neuro-fuzzy approach using lolimot learning algorithm, is compared with multilayer perceptron (MLP), and Radial Basis Function (RBF). In the meantime, various versions of Artificial Immune Recognition Systems (AIRS) including AIRS1, AIRS2, Parallel AIRS and Modified AIRS with Fuzzy K-Nearest neighbor (Fuzzy-KNN) are also evaluated in comparison with the above mentioned algorithms to classify the same text. The experimental results of classification on a dataset of 540 data reveals that, due to the distributed characteristics of a text, and the complexity of tasks respectively, evolutionary and neuro-fuzzy methods are expected to be particularly workable and successfully applicable to task-oriented text classification specifically for the purpose of decision support.

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“…DM is a metabolic disorder which characterizes the inability of the pancreas to control blood glucose concentration. This problem results in blood glucose levels moving out of normal range [1].The classification of diabetes falls under 3 categories: …”

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confidence: 99%

Introduction to Diabetes Related Causes and Overview of Image Processing Methods

Kumar

Shaik²

2015

Image Processing in Diabetic Related Causes

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Diabetes mellitus (DM) often simply known as diabetes-is a group of metabolic diseases. The most burning problem in India is 'Metabolic Syndrome'. One of these is the DM, calling India the diabetes Capital of the world as there is an alarming rise in prevalence of diabetes, which has gone beyond epidemic to the pandemic one. For every 10 s a person dies from diabetic related causes in the world. DM is a metabolic disorder which characterizes the inability of the pancreas to control blood glucose concentration. This problem results in blood glucose levels moving out of normal range [1].The classification of diabetes falls under 3 categories:

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Hierarchical Takagi-Sugeno type fuzzy system for diabetes mellitus forecasting

Cited by 21 publications

References 15 publications

An intelligent approach for optimal regulation of blood glucose level

An intelligent approach for optimal regulation of blood glucose level

A neuro-fuzzy immune inspired classifier for task-oriented texts

Introduction to Diabetes Related Causes and Overview of Image Processing Methods

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