Article
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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
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Version 1
: Received: 30 October 2019 / Approved: 30 October 2019 / Online: 30 October 2019 (05:05:01 CET)
Version 2 : Received: 22 February 2020 / Approved: 24 February 2020 / Online: 24 February 2020 (04:10:49 CET)
Version 2 : Received: 22 February 2020 / Approved: 24 February 2020 / Online: 24 February 2020 (04:10:49 CET)
How to cite: Mojrian, S.; Pinter, G.; Hassannataj Joloudari, J.; Felde, I.; Szabo-Gali, A.; Nadai, L.; Mosavi, A. Hybrid Machine Learning Model of Extreme Learning Machine Radial Basis Function for Breast Cancer Detection and Diagnosis: A Multilayer Fuzzy Expert System . Preprints 2019, 2019100349 (doi: 10.20944/preprints201910.0349.v2). Mojrian, S.; Pinter, G.; Hassannataj Joloudari, J.; Felde, I.; Szabo-Gali, A.; Nadai, L.; Mosavi, A. Hybrid Machine Learning Model of Extreme Learning Machine Radial Basis Function for Breast Cancer Detection and Diagnosis: A Multilayer Fuzzy Expert System . Preprints 2019, 2019100349 (doi: 10.20944/preprints201910.0349.v2).
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.
Keywords
hybrid machine learning; extreme learning machine (ELM); radial basis function (RBF); breast cancer; support vector machine (SVM)
Subject
MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Amir Mosavi
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