Abstract
Breast cancer is a leading cause of mortality in women all over the world. According to the worldwide cancer statistics, early detection and treatment are keys components for improving the recovery rate of breast cancer and lowering the death rate. Machine learning solutions have been proved to be particularly very successful in exploring the origins of such severe diseases, which requires processing vast amounts of data.
In the present study, robust grey wolf optimisation-Random Forest (RGWO-RF) approach was proposed. Our proposed approach based on two steps feature selection process and classification. Modified Grey Wolf Optimizer is used to locate and determine the most significant features. Then, utilizing the prior optimum selections of features, by using Random Forest (RF) classifier to classify breast cancer disease. The reason for using RF it’s robustness and highest accuracy.
We apply the proposed approach on Wisconsin Diagnostic Breast Cancer (WDBC) database. The experimental result improve that the hybridation between RGWO for feature selection and RF classifier increase the accuracy rate of classification and demonstrating it’s robustness in identifying the breast cancer.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394–424 (2018)
Ades, F., et al.: Luminal breast cancer: Molecular characterization, clinical management, and future perspectives. J. Clin. Oncol. 32, 2794–2803 (2014)
Dua, D., Gra®, C.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2019). http://archive.ics.uci.edu/ml
Zheng, B., Yoon, S.W., Lam, S.S.: Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Syst. Appl. 41(4), 1476–1482 (2014)
Pritom, A.I., Munshi, M.A.R., Sabab, S.A., Shihab, S.: Predicting breast cancer recurrence using effective classification and feature selection technique. In: 2016 19th International Conference on Computer and Information Technology (ICCIT), pp. 310–314. IEEE, New York (2016)
Huang, M.W., Chen, C.W., Lin, W.C., Ke, S.W., Tsai, C.F.: SVM and SVM ensembles in breast cancer prediction. PLoS One 12(1), e0161501 (2017)
Dora, L., Agarwal, S., Panda, R., Abraham, A.: Optimal breast cancer classification using Gauss-Newton representation based algorithm. Expert Syst. Appl. 85, 134–145 (2017)
Shahnaz, C., Hossain, J., Fattah, S.A., Ghosh, S.: Efficient approaches for accuracy improvement of breast cancer classification using Wisconsin database. In: IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (2017)
Li, Q., et al.: An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis. Comput. Math. Methods Med. 2017, 1–15 (2017). https://doi.org/10.1155/2017/9512741
Liu, N., Qi, E., Xu, M., Liu, G.: A novel intelligent classification model for breast cancer diagnosis. Inf. Process. Manage. 56, 609–623 (2019)
Sayed, G.I., Hassanien, A.E., Azar, A.T.: Feature selection via a novel chaotic crow search algorithm. Neural Comput. Appl. 31(1), 171–188 (2017). https://doi.org/10.1007/s00521-017-2988-6
Rao, H., Shi, X., Rodrigue, A., Feng, J., Xia, Y.: Feature selection based on artificial bee colony and gradient boosting decision tree. Appl. Soft Comput. 74, 634–642 (2019)
Abdel-Basset, M., El-Shahat, D., El-Henawy, I., Mirjalili, S.: A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection. Expert Syst. Appl. 139, 112824 (2020)
Lim, T.S., Tay, K.G., Huong, A., Lim, X.Y.: Breast cancer diagnosis system using hybrid support vector machine-artificial neural network. Int. J. Electr. Comput. Eng. 11(4), 3059 (2021). https://doi.org/10.11591/ijece.v11i4.pp3059-3069
Kumar, S., Singh, M.: Breast cancer detection based on feature selection using enhanced grey wolf optimizer and support vector machine algorithms. Vietnam J. Comput. Sci. 8(2), 177–197 (2021)
Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mezaghrani, A., Debakla, M., Djemal, K. (2023). Robust Method for Breast Cancer Classification Based on Feature Selection Using RGWO Algorithm. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-28540-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28539-4
Online ISBN: 978-3-031-28540-0
eBook Packages: Computer ScienceComputer Science (R0)