Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Hybrid Machine Learning Model of Extreme Learning Machine Radial Basis Function for Breast Cancer Detection and Diagnosis: A Multilayer Fuzzy Expert System

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)

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.

Subject Areas

hybrid machine learning; extreme learning machine (ELM); radial basis function (RBF); breast cancer; support vector machine (SVM)

Comments (1)

Comment 1
Received: 24 February 2020
Commenter: Amir Mosavi
Commenter's Conflict of Interests: Author
Comment: The paper has been revised. One author has been replaced.
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