Medical Diagnostic System Basing Fuzzy Rough Neural-Computing for Breast Cancer

Gamal, Mona

doi:10.1007/978-3-319-48308-5_45

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…In addition, Gafar (2016) proposed a feature selection approach based on fuzzy rough neural networks to solve diagnostic breast cancer problems, which could remove unnecessary features from medical data. The lower and upper approximations of the input features were weighted by input synapses learnt through the training phase.…”

Section: Fuzzy Rough Neural Network Based Feature Selection Methodsmentioning

confidence: 99%

Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

Pang

Jia

et al. 2021

WIREs Data Min & Knowl

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Feature selection aims to select a feature subset from an original feature set based on a certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it has become a key method for data preprocessing in many data mining tasks. Recently, many feature selection strategies have been developed since in most cases it is infeasible to obtain an optimal/reduced feature subset by using exhaustive search. Among these strategies, fuzzy rough set theory has proved to be an ideal candidate for dealing with uncertain information. This article provides a comprehensive review on the fuzzy rough set theory and two fuzzy rough set theory based feature selection methods, that is, fuzzy rough set based feature selection methods and fuzzy rough neural network based feature selection methods. We review the publications related to the fuzzy rough theory and its applications in feature selection. In addition, the challenges in the two types of feature selection methods are also discussed. This article is categorized under: Technologies > Machine Learning

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Section: Fuzzy Rough Neural Network Based Feature Selection Methodsmentioning

confidence: 99%

Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review

Pang

Jia

et al. 2021

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…Gafar [ 31 ] proposes a diagnosing system of breast cancer using a hybrid of fuzzy rough feature selection and RNN. The fuzzy rough feature selection algorithm is used to find the best reduction, and the RNN is trained by the reduced dataset to learn the connection weights iteratively.…”

Section: Related Work and Preliminariesmentioning

confidence: 99%

“…RNNs are a combination of rough set theory and NN to benefit from their advantages. RNNs [ 10 , 11 , 31 ] are inspired by the concepts of traditional NN in both their learning algorithm and structure of connections. The essential difference is the neuron, which is used in RNN formed from a pair of neurons.…”

Section: Related Work and Preliminariesmentioning

confidence: 99%

Intelligent Neutrosophic Diagnostic System for Cardiotocography Data

Amin

Salama

El-Henawy

et al. 2021

Computational Intelligence and Neuroscience

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Cardiotocography data uncertainty is a critical task for the classification in biomedical field. Constructing good and efficient classifier via machine learning algorithms is necessary to help doctors in diagnosing the state of fetus heart rate. The proposed neutrosophic diagnostic system is an Interval Neutrosophic Rough Neural Network framework based on the backpropagation algorithm. It benefits from the advantages of neutrosophic set theory not only to improve the performance of rough neural networks but also to achieve a better performance than the other algorithms. The experimental results visualize the data using the boxplot for better understanding of attribute distribution. The performance measurement of the confusion matrix for the proposed framework is 95.1, 94.95, 95.2, and 95.1 concerning accuracy rate, precision, recall, and F1-score, respectively. WEKA application is used to analyse cardiotocography data performance measurement of different algorithms, e.g., neural network, decision table, the nearest neighbor, and rough neural network. The comparison with other algorithms shows that the proposed framework is both feasible and efficient classifier. Additionally, the receiver operation characteristic curve displays the proposed framework classifications of the pathologic, normal, and suspicious states by 0.93, 0.90, and 0.85 areas that are considered high and acceptable under the curve, respectively. Improving the performance measurements of the proposed framework by removing ineffective attributes via feature selection would be suitable advancement in the future. Moreover, the proposed framework can also be used in various real-life problems such as classification of coronavirus, social media, and satellite image.

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