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Mammography is often used as the most common laboratory method for thedetection of breast cancer, yet associated with the high cost and many sideeffects. Machine learning prediction as an alternative method has shownpromising results. This paper presents a method based on a multilayer fuzzyexpert system for the detection of breast cancer using an extreme learningmachine (ELM) classification model integrated with radial basis function (RBF)kernel called ELM-RBF, considering the Wisconsin dataset. The performance ofthe proposed model is further compared with a linear-SVM model. The proposedmodel 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 termsof criteria of accuracy, precision, sensitivity, specificity, validation, truepositive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for thesecriteria presents better performance compared to the SVM model.

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