Irvan Masoudiasl; Shaghayeh Vahdat; Somayeh Hessam; Shahaboddin Shamshirband; Hamid Alinejad-Rokny
Volume 21, Issue 9 , 2019, Pages 1-11
Abstract
Background: Breast cancer is the most common cancer in women, which has not been completely cured yet. The traditional ap- proaches have low accuracy for breast cancer detection. However, intelligent techniques have been recently used in medical re- search to distinguish infected individuals from healthy ...
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Background: Breast cancer is the most common cancer in women, which has not been completely cured yet. The traditional ap- proaches have low accuracy for breast cancer detection. However, intelligent techniques have been recently used in medical re- search to distinguish infected individuals from healthy ones, accurately.Objectives: In this study, we aim to develop an ensemble machine learning (ML) method to distinguish tumor samples from healthy samples robustly.Methods: We used an Imperial Competitive Algorithm coupled with a Fuzzy System (ICA-Fuzzy-SR) to identify the most influencing features to recognize tumor samples. To evaluate the proposed method, we used the publicly available Wisconsin Breast Cancer Dataset (WBCD).Results: Benchmarking with the current existing leading methods indicates that our proposed method achieves 95.45% prediction accuracy, which is 3% better than those reported in previous studies.Conclusions: Such results achieve while our model is significantly faster than previously proposed models to solve this problem.
