Proposing an Integrated Method based on Fuzzy Tuning and ICA Techniques to Identify the Most Influencing Features in Breast Cancer
نویسندگان
1 Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
3 Department of Healthcare Services Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
4 Department of Health Services Administration, South Tehran Branch, Islamic Azad University, Tehran, Iran
5 Department of Health Services Administration, South Tehran Branch, Islamic Azad University, Tehran, Iran
doi
10.5812/ircmj.92077چکیده
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.
