Abstract
One of the most common diseases among the middle-aged women is breast cancer. The proposed system designed an efficient breast cancer detection and classification approach using an ensemble booster algorithm “XGBOOST” classifier. All the traditional approaches in machine learning have developed models with low variance or with high bias. So, the proposed system has evaluated the model with different evaluation metrics. In the world of machine learning, optimization has a great impact. To address this issue, the proposed system performed whirling of hyper-parameters during the classification process. Finally, the designed system is compared with conventional models. The major goal of the proposed system is to identify the breast cancer and classify the stage of cancer. So, the automated system can help the doctors to recommend the treatment or medicines and in turn the morality rate due to the breast cancer can be reduced.
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References
Anila, M., Pradeepini, G.: Study of prediction algorithms for selecting appropriate classifier in machine learning. J. Adv. Res. Dyn. Control Syst. 9(18), 257–268 (2017)
Kumar, P., NVRao, K., Narasimha Raju, A, NagendraKumar, D.J.: Leaf classification based on shape and edge feature with k-NN classifier. In: 2nd International Conference on Contemporary Computing and Informatics (IC3I), pp.548–552. IEEE, Greater Noida, India, (2016)
Amandeep, K., Prabhjeet, K.: Breast cancer detection and classification using analysis and gene-back proportional neural network algorithm. Int. J. Innovative Technol. Exploring Eng. 8(8), 2798–2803 (2019)
Assegie, T.: An optimized K-nearest neighbor based breast cancer detection. J. Robot. Control 2(3), 115–118 (2021)
Pavithra, S., Vanithamani, R.: Breast cancer detection using random forest classifier. In: Jospeh Raj, A.N., Vijayalakshmi, G. (eds.), Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments, pp. 85–98. IGI Global Publishers (2021)
Kumar, P., Maddireddi, P., Anantha Lakshmi, V., Kumar, D.J.N.: Novel fuzzy classification approaches based on optimisation of association rules. In: 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 1–5. IEEE, SJB Institute of Technology, Bengaluru, Karnataka, India (2016)
Sharma, A., Kochar, B., Joshi, N., Kumar, V.: Breast cancer detection using deep learning and machine learning: a comparative analysis. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds.) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol. 1165, pp. 503–514. Springer, Singapore (2021)
Tahmooresi, M., Afshar, A., Bashari, R., Nowshath, K.B., Bamiah, M.A.: Early detection of breast cancer using machine learning techniques. J. Telecommun. Electron. Comput. Eng. 10(3), 21–27 (2018)
Aslan, M.F., Celik, Y., Sabanci, K., Durdu, A.: Breast cancer diagnosis by different machine learning methods using blood analysis data. Int. J. Intell. Syst. Appl. Eng. 6(4), 289–293 (2018)
Minh, H.L., Mai Van, M., Lang, T.V.: Deep feature fusion for breast cancer diagnosis on histopathology images. In: 11th International Conference on Knowledge and Systems Engineering (KSE), pp. 1–6. IEEE, Da Nang, Vietnam (2019)
HimaBindu, Ch., Sridevi, G.: Transform domain analysis of multimodal medical image fusion. In : International Conference on Computer Vision, High Performance Computing, Smart Devices and Networks (CHSN 2020), vol. 1074. IOP Conference Serial: Material Science Engineering (2020)
Lahoura, V., Singh, H., Aggarwal, A., Sharma, B., Mohammed, M.A., Damaševičius, R., Kadry, S., Cengiz, K.: Cloud computing-based framework for breast cancer diagnosis using extreme learning machine. Diagnostics 11(2), 241 (2021)
Song, R., Li, T., Wang, Y.: Mammographic classification based on XGBoost and DCNN With multi features. IEEE Access 8, 75011–75021 (2020)
Priyanka. K.S.: A review paper on breast cancer detection using deep learning. In: 1st International Conference on Computational Research and Data Analytics (ICCRDA 2020) vol.1022, pp. 1–7. IOP Conference Serial: Material Science Engineering (2021)
UCI Machine Learning Repository. http://archive.ics.uci.edu/ml. Last Accessed on 9 March 2021
Balyan A., Singh Y., Shashank: classifying breast cancer based on machine learning. In: Bansal, P., Tushir, M., Balas, V., Srivastava, R. (eds), Proceedings of International Conference on Artificial Intelligence and Applications. Advances in Intelligent Systems and Computing, vol. 1164. Springer, Singapore (2021)
Punitha, S., Al-Turjman, F, Thompson, S.: An automated breast cancer diagnosis using feature selection and parameter optimization in ANN. Comput. Electr. Eng. 90(2) (2021)
Mohammad M.G., Sohrab, Z.: Application of decision tree-based ensemble learning in the classification of breast cancer. Comput. Biol. Med. 128, 104089 (2021)
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Kumar, P.S., Neti, P., Kumar, D.J.N., Murthy, G.S.N., Lalitha, R.V.S., Kalyan Ram, M. (2022). OXGBoost: An Optimized eXtreme Gradient Boosting Algorithm for Classification of Breast Cancer. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_4
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DOI: https://doi.org/10.1007/978-981-19-0840-8_4
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