Unsupervised fuzzy cognitive map in diagnosis of breast epithelial lesions

Amirkhani, Abdollah; Mosavi, Mohammad Reza; Naimi, Azar

doi:10.1109/icbme.2015.7404127

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…There are several methods for converting fuzzy linguistic variables because using triangular and trapezoidal membership functions in the medical context is more common than other membership functions [5,[11][12]. The present study used triangular and trapezoidal membership functions, such that the membership functions of the output concept of risk rate is shown in Figure 2.…”

Section: Simulation Results Of the Proposed Methodsmentioning

confidence: 99%

“…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%

“…In this method, total accuracy was obtained by 94.3%. Also, in one study [11], the classification of breast lesions was proposed based on 10 major histological features in 3 groups of UDH, ADH and DCIS on 86 cases. In this study, the accuracy of UDH classification was determined to be 88% and 86% for ADH and DCIS, respectively, UDH was considered as a benign lesion, and ADH and DCIS were considered as malignant lesions.…”

Section: Introductionmentioning

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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of stroke probability occurrence based on fuzzy cognitive maps

et al. 2019

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“…In this method, total accuracy was obtained by 94.3%. Also, in one study [14], the classification of breast lesions was proposed based on ten major histological features in three groups of UDH, ADH, and DCIS on 86 cases. In this study, the accuracy of UDH classification was determined to be 88 and 86% for ADH and DCIS, respectively, UDH was considered as benign lesion, and ADH and DCIS were considered as malignant lesions.…”

Section: Introductionmentioning

confidence: 99%

“…It can be employed in diagnosis, forecasting, classification, and decision making [4]. Using FCM and non‐linear Hebbian learning (NHL) algorithm, various diseases have been diagnosed so far such as Autism [12], Parkinson [13], breast cancer [5, 14], and celiac disease [15].…”

Section: Introductionmentioning

confidence: 99%

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

Khodadadi

Shayanfar

Maghooli

et al. 2019

IET Systems Biology

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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.

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