Abstract
Breast cancer is one of the most common types of cancer. For this reason, it is very important to diagnose breast cancer. In this paper, a type-2 fuzzy multiplication wavelet neural network model is proposed to classify the Wisconsin Breast Cancer dataset. In this model, Shannon wavelet function is used as the type-2 membership function and the multiplication of the Shannon wavelet functions is used in the conclusion part of the rules. The results of proposed model is compared with type-1 fuzzy multiplication wavelet neural network, multilayer perceptron network, radial basis function network, Bayesian network learning, and decision tree algorithm. It can be seen that proposed type-2 fuzzy multiplication wavelet neural network model gives the best results among these algorithms.
Highlights
Breast cancer is one of the leading cancer types that cause women to die
Some statistical feature selection methods are applied to breast cancer dataset in order to select categorical features and these features are refined by particle swarm optimization and bagged decision tree is used for classification
This paper proposes a type-2 fuzzy multiplication wavelet neural network model to classification of breast cancer
Summary
Breast cancer is one of the leading cancer types that cause women to die. Even a medical specialist is sometimes difficult to diagnose the cancer after performing various tests. Type-2 wavelet membership functions were used in fuzzy rules Another model for prediction and classification of various systems is fuzzy wavelet neural networks (FWNN), which combines fuzzy neural networks with wavelet functions. This paper proposes a type-2 fuzzy multiplication wavelet neural network model to classification of breast cancer. The new model is named as type-2 fuzzy multiplication wavelet neural network (T2FWNN) and used for identification of dynamical plants in [34]. The model in [34] is updated to type-2 fuzzy multiplication wavelet neural network (T2FMWNN) in order to achieve classification problems and is used for classification of breast cancer dataset. The proposed new T2FMWNN model gives better results according to type-1 FMWNN (T1FMWNN), multilayer perceptron (MLP) network, radial basis function network (RBF) and FURIA in breast cancer classification
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