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Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System

EasyChair Preprint no. 1824

7 pagesDate: November 4, 2019

Abstract

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R2, MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

Keyphrases: breast cancer, breast cancer dataset, Breast Cancer Detection, cancer mass, Confusion Matrix, cross validation technique, Data Mining, Data Mining Technique, developed country, ELM model, elm rbf model, epithelial cell size, evaluation criterion, expert system, Extreme Learning Machine, Extreme Learning Machine (ELM), first module, fold cross, Fuzzy Linguistic Variable, fuzzy rule, fuzzy system, hybrid machine learning, linguistic variable, machine learning, Malignant breast cancer, multilayer fuzzy expert system, negative rate, neural network, Radial Basis Function, Radial Basis Function (RBF), rmse r2 mape model, Support Vector Machine, Support Vector Machine (SVM), Wisconsin Dataset

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:1824,
  author = {Sanaz Mojrian and Gergő Pintér and Javad Hassannataj Joloudari and Imre Felde and Narjes Nabipour and László Nádai and Amir Mosavi},
  title = {Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System},
  howpublished = {EasyChair Preprint no. 1824},

  year = {EasyChair, 2019}}
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