Modeling of Parkinson's Disease Using Fuzzy Cognitive Maps and Non-Linear Hebbian Learning

Anninou, Antigoni P.; Groumpos, Peter P.

doi:10.1142/s0218213014500109

Cited by 17 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where € is the tolerance level minimising the changes in the DOC values. When the conditions for termination of the algorithm are met, the final weight matrix, W NHL , is obtained [13,15]. Algorithm flowchart is shown in Fig.…”

Section: Nhl Algorithmmentioning

confidence: 99%

Fuzzy cognitive map based approach for determining the risk of ischemic stroke

Khodadadi

Shayanfar

Maghooli

et al. 2019

IET Systems Biology

View full text Add to dashboard Cite

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non-linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10-fold cross-validation, for 110 real cases, and the results were compared with those of support vector machine and K-nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non-commercial purposes.

show abstract

Section: Nhl Algorithmmentioning

confidence: 99%

Fuzzy cognitive map based approach for determining the risk of ischemic stroke

Khodadadi

Shayanfar

Maghooli

et al. 2019

IET Systems Biology

View full text Add to dashboard Cite

show abstract

“…These models involve updating the node state values and the causal relationships between concepts to simulate dynamic behavior. A number of weight-learning methods, such as Hebbian learning [26,27], genetic algorithm (GA) [28], and swarm intelligence optimization algorithm [29], have been applied to learning weights of an FCM. However, most of these methods require domain experts who can specify in advance the initial weight matrix of an FCM.…”

Section: Fuzzy Cognitive Mapmentioning

confidence: 99%

“…Most learning algorithms [26,27] for FCMs require domain experts who can predetermine the initial weight matrix. Moreover, GA [28] and swarm intelligence optimization algorithms [29] present low running speeds and induce network instability.…”

Section: Proposed Learning Algorithm For Fcmsmentioning

confidence: 99%

A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

Zhang

Sun

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between emotions and certain mathematical derivations to determine the network structure. The proposed algorithm can handle a large number of concepts, whereas a typical FCM can handle only relatively simple networks (maps). Different acoustic features, including fundamental speech features and a new spectral feature, are extracted to evaluate the performance of the proposed method. Three experiments are conducted in this paper, namely, single feature experiment, feature combination experiment, and comparison between the proposed algorithm and typical networks. All experiments are performed on TYUT2.0 and EMO-DB databases. Results of the feature combination experiments show that the recognition rates of the combination features are 10%-20% better than those of single features. The proposed FCM learning algorithm generates 5%-20% performance improvement compared with traditional classification networks.

show abstract

“…There have been many studies in medicine over the past years using the fuzzy cognitive map and nonlinear Hebbian learning algorithm method. These studies have included the classification of the autism disorder [9], modelling the Parkinson's disease [10], classification of breast lesions [5,11], and Grading celiac disease [12]. In a previous study [9], the onset of childhood autism was predicted with regard to 23 major factors of the disease, such as enjoy being swung, take an interest in other children, and climbing on things.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the disease was classified into 3 categories (definite autism, no autism, probable autism), and the system accuracy was reported by 79.9%. In another study [10], Parkinson mathematical modelling was provided based on 6 main factors of the disease, such as tremor, rigidity, and posture. Then, the disease was classified into 6 stages (healthy, stage 1, stage 2, stage 3, stage 4, stage 5 and stage 6), the obtained results were compared and simulated with and without the use of NHL algorithm.…”

Section: Introductionmentioning

confidence: 99%

Prediction of stroke probability occurrence based on fuzzy cognitive maps

et al. 2019

View full text Add to dashboard Cite

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)%.

show abstract

Modeling of Parkinson's Disease Using Fuzzy Cognitive Maps and Non-Linear Hebbian Learning

Cited by 17 publications

References 8 publications

Fuzzy cognitive map based approach for determining the risk of ischemic stroke

Fuzzy cognitive map based approach for determining the risk of ischemic stroke

A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

Prediction of stroke probability occurrence based on fuzzy cognitive maps

Contact Info

Product

Resources

About