A theoretical mathematical modeling of Parkinson's disease using Fuzzy Cognitive Maps

Groumpos, Peter P.; Anninou, Antigoni P.

doi:10.1109/bibe.2012.6399748

Cited by 25 publications

(10 citation statements)

References 7 publications

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“…As it can be observed, the value of DOC reaches to 0.67217. This gives a risk of 34.4% according to the equation in [22], and it is a high risk according to the neurologists. Therefore, the nonlinear Hebbian learning algorithm is used to adjust the relations between the factors so that the system response approaches the real value.…”

Section: Simulation Results Of the Proposed Methodsmentioning

confidence: 99%

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Prediction of stroke probability occurrence based on fuzzy cognitive maps

et al. 2019

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Among neurological patients, stroke is the most common cause of mortality. It is a health problem that is very costly all over the world. Therefore, the mortality due to the disease can be reduced by identifying and modifying the risk factors. Controllable factors which are contributing to stroke including hypertension, diabetes, heart disease, hyperlipidemia, smoking, and obesity. Therefore, by identifying and controlling the risk factors, stroke can be prevented and the effects of this disease could be reduced to a minimum. Therefore, for the quick and timely diagnosis of the disease, we need an intelligent system to predict the stroke risk. In this paper, a method has been proposed for predicting the risk rate of stroke which is based on fuzzy cognitive maps and nonlinear Hebbian learning algorithm. The accuracy of the proposed NHL-FCM model is tested using 15-fold cross-validation, for 90 actual cases, and compared with those of support vector machine and k-nearest neighbours. The proposed method shows superior performance with a total accuracy of (95.4 ± 7.5)%.

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Section: Simulation Results Of the Proposed Methodsmentioning

confidence: 99%

“…Example 2: In this example, a subject with Blood pressure (140), Cholesterol (190), Blood sugar (90), Heart disease (No), Smoking (Yes) and BMI (22) was examined. Neurologists predict the risk rate of stroke for this person as "low".…”

Section: Simulation Results Of the Proposed Methodsmentioning

confidence: 99%

Prediction of stroke probability occurrence based on fuzzy cognitive maps

et al. 2019

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“…In other words they recommend a modeling process consisting of an array of interconnected and interdependent nodes Ci (variables), as well as the relationships between them W (weights).Concepts take values in the interval [0,1] and weights belong in the interval [-1,1]. A more comprehensive mathematical presentation of FCMs is provided in [14], [15], [16], [17], [18].…”

Section: Basics Of Fuzzy Cognitive Maps (Fcm)mentioning

confidence: 99%

Why Modeling Complex Dynamic Systems using Fuzzy Cognitive Maps?

Groumpos

2017

IJBTE

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The difficult problem of modeling Complex Dynamic Systems (CDS) is carefully reviewed. Main characteristics of CDS are considered and analyzed. Today’s mathematical models and approaches cannot provide satisfactory answers to the challenging problems of the society. The key problem of complex dynamic systems and control theory consists in the development of methods of qualitative analysis of the dynamics and behavior of such systems and in the construction of efficient control algorithms for their efficient operation. The purpose of control to bring the system to a point of its phase space which corresponds to maximal or minimal value of the chosen efficiency criterion is reviewed and analyzed. The reasons for using Fuzzy Cognitive Maps (FCMs) in modeling Complex dynamic Systems are provided. The basics of FCMs are briefly presented. An illustrative example is considered and interesting results are presented and discussed.

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“…To create a more realistic frequency distribution, each frequency band (VLF, LF, and HF) is simulated by three sub-oscillators. Thereby, each sub-oscillator is determined by its fundamental frequency f i (randomly chosen in the respective frequency range) and its amplitude a i , randomly chosen in the range of [20,40]ms. Finally, the cardiac control system m(t) is given by…”

Section: Simulation Of Hrv Time Seriesmentioning

confidence: 99%

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2017

SNE

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EUROSIM Congresses are the most important modelling and simulation events in Europe. For EUROSIM 2019, we are soliciting original submissions describing novel research and developments in the following (and related) areas of interest: Continuous, discrete (event) and hybrid modelling, simulation, identification and optimization approaches. Two basic contribution motivations are expected: M&S Methods and Technologies and M&S Applications. Contributions from both technical and non-technical areas are welcome. Congress Topics The EUROSIM 2019 Congress will include invited talks, parallel, special and poster sessions, exhibition and versatile technical and social tours. The Congress topics of interest include, but are not limited to:

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A theoretical mathematical modeling of Parkinson's disease using Fuzzy Cognitive Maps

Cited by 25 publications

References 7 publications

Prediction of stroke probability occurrence based on fuzzy cognitive maps

Prediction of stroke probability occurrence based on fuzzy cognitive maps

Why Modeling Complex Dynamic Systems using Fuzzy Cognitive Maps?

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