Abstract
Nowadays, breast cancer is one of the most ordinary cancers for women around the world. When breast cells grow and divide in an unrestrained way than forming a mass of tissue that call a tumor and then happen breast cancer. Many researchists have applied many applications to diagnose breast cancer. In this paper, a population-based metaheuristic named chemical reaction optimization (CRO) has been used to optimize the number of features. We have applied metaheuristic algorithms along with machine learning methods to predict breast cancer. From the experimental results, it can be observed that the SVM classifier gives the highest accuracy and f1-score among four classifiers (SVM, XGBoost, random forest, and decision tree). The result of the comparison showed that both the f1-score and accuracy are better than the related methods. To find out the best results on the detection of breast cancer using chemical reaction optimization (CRO) and a minimal number of features is the main target of our paper. We use SVM, decision tree, XGBoost, and random forest as the classifiers. For the experiment, the UCI machine learning dataset has been used. We have tried to find the best results in terms of measurement metrics using the proper technique.
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References
Breast Cancer. https://www.who.int/news-room/fact-sheets/detail/breast-cancer. Accessed 14 Feb 2023
Types of Breast Cancer. https://www.nationalbreastcancer.org/types-of-breast-cancer. Accessed 14 Feb 2023
Wu J, Hicks C (2021) Breast cancer type classification using machine learning. J Pers Med 11(2):61. https://doi.org/10.3390/jpm11020061
Pratap U, Chhabra S (2021) Breast cancer prediction using different machine learning algorithms. In: 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), pp 451–454. IEEE, Greater Noida, India. https://doi.org/10.1109/ICAC3N53548.2021.9725688
Amrane M, Oukid S, Gagaoua I, Ensarİ T (2018) Breast cancer classification using machine learning. In: Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), IEEE, Istanbul, Turkey. https://doi.org/10.1109/EBBT.2018.8391453
Mitra D, Sharma N, Rashid M, Singh R (2022) Classification rules based breast cancer detection using machine learning approach. In: 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, Uttar Pradesh, India, pp 1274–1278. https://doi.org/10.1109/IC3I56241.2022.10072832
Konstantina K, Themis PE, Konstantinos PE, Michalis VK, Dimitrios IF (2015) Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13
Lam A, Li VOK (2012) Chemical reaction optimization: a tutorial. Memetic Comp 4:3–17 (Springerlink). https://doi.org/10.1007/s12293-012-0075-1
Mohammed SA, Darrab S, Noaman SA, Saake G (2020) Analysis of breast cancer detection using different machine learning techniques. In: International conference on data mining and big data, data mining and big data, pp 108–117. https://doi.org/10.1007/978-981-15-7205-0_10
Islam MM, Haque MR, Iqbal H, Hasan MM, Hasan M, Kabir MN (2020) Breast cancer prediction: a comparative study using machine learning techniques. SN Comput Sci 1:290. https://doi.org/10.1007/s42979-020-00305-w
Sengar PP, Gaikwad MJ, Nagdive AS (2020) Comparative study of machine learning algorithms for breast cancer prediction. In: Third International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE, Tirunelveli, India. https://doi.org/10.1109/ICSSIT48917.2020.9214267
Marne S, Churi S, Marne M (2020) Predicting breast cancer using effective classification with decision tree and K means clustering technique. In: International conference on Emerging Smart Computing and Informatics (ESCI), IEEE, Pune, India. https://doi.org/10.1109/ESCI48226.2020.9167544
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Majumder, S., Rafiqul Islam, M. (2024). Breast Cancer Prediction Using Chemical Reaction Optimization and Classifier. In: Arefin, M.S., Kaiser, M.S., Bhuiyan, T., Dey, N., Mahmud, M. (eds) Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning. BIM 2023. Lecture Notes in Networks and Systems, vol 867. Springer, Singapore. https://doi.org/10.1007/978-981-99-8937-9_68
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DOI: https://doi.org/10.1007/978-981-99-8937-9_68
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