Integrating Feature Selection and Machine Learning Boosting for Accurate Breast Cancer Prediction

Wisal Hashim Abdulsalam, Ruma Kareem K. Ajeena and Mohammed Ayad Saad

References:

1. S. K. Mishra, B. Appasani, A. Pati, et al., “Category boosting machine learning algorithm for breast cancer prediction,” Revue Roumaine des Sciences Techniques—Série Électrotechnique et Énergétique, vol. 66, pp. 201–206, 2021.
2. M. Kumar, S. Singhal, S. Shekhar, B. Sharma, and G. Srivastava, “Optimized stacking ensemble learning model for breast cancer detection and classification using machine learning,” Sustainability, vol. 14, p. 13998, 2022, doi: 10.3390/su142113998.
3. S. Zhou, C. Hu, S. Wei, and X. Yan, “Breast cancer prediction based on multiple machine learning algorithms,” Technology in Cancer Research & Treatment, vol. 23, p. 15330338241234791, 2024, doi: 10.1177/15330338241234791.
4. J. Zhu, Z. Zhao, B. Yin, C. Wu, C. Yin, R. Chen, et al., “An integrated approach of feature selection and machine learning for early detection of breast cancer,” Scientific Reports, vol. 15, p. 13015, 2025, doi: 10.1038/s41598-025-97685-x.
5. H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, et al., “Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 71, pp. 209–249, 2021, doi: 10.3322/caac.21660.
6. F. Bray, M. Laversanne, H. Sung, J. Ferlay, R. L. Siegel, I. Soerjomataram, et al., “Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 74, pp. 229–263, 2024, doi: 10.3322/caac.21834.
7. R. Rabiei, S. M. Ayyoubzadeh, S. Sohrabei, M. Esmaeili, and A. Atashi, “Prediction of breast cancer using machine learning approaches,” Journal of Biomedical Physics & Engineering, vol. 12, p. 297, 2022, doi: 10.31661/jbpe.v0i0.2109-1403.
8. A. A. Jasim, A. A. Jalal, N. M. Abdulateef, and N. A. Talib, “Effectiveness evaluation of machine learning algorithms for breast cancer prediction,” Bulletin of Electrical Engineering and Informatics, vol. 11, pp. 1516–1525, 2022, doi: 10.11591/eei.v11i3.3621.
9. V. Nemade and V. Fegade, “Machine learning techniques for breast cancer prediction,” Procedia Computer Science, vol. 218, pp. 1314–1320, 2023, doi: 10.1016/j.procs.2023.01.110.
10. D. Kumari, M. V. S. S. Naidu, S. Panda, and J. Christopher, “Predicting breast cancer recurrence using deep learning,” Discover Applied Sciences, vol. 7, p. 113, 2025, doi: 10.1007/s42452-025-06512-5.
11. A. J. Ferreira and M. A. Figueiredo, “Boosting algorithms: A review of methods, theory, and applications,” in Ensemble Machine Learning: Methods and Applications, pp. 35–85, 2012, doi: 10.1007/978-1-4419-9326-7_2.
12. S. H. Jadoaa, R. H. Ali, W. H. Abdulsalam, and E. M. Alsaedi, “The impact of feature importance on spoofing attack detection in IoT environment,” Mesopotamian Journal of CyberSecurity, vol. 5, pp. 240–255, 2025, doi: 10.58496/MJCS/2025/016.
13. S. S. Abinayaa, P. Arumugam, D. B. Mohan, A. Rajendran, A. Lashab, B. Wei, et al., “Securing the edge: CatBoost classifier optimized by the lyrebird algorithm to detect denial of service attacks in internet of things-based wireless sensor networks,” Future Internet, vol. 16, p. 381, 2024, doi: 10.3390/fi16100381.
14. R. D. Abdu-Aljabara, K. D. Aljafaara, Z. J. M. Ameenb, and H. A. Namanc, “A comparative study of breast cancer detection and recurrence prediction using CatBoost classifier,” Acta Polytechnica, vol. 65, pp. 136–142, 2025, doi: 10.14311/AP.2025.65.0136.
15. P. Sarker, A. Ksibi, M. M. Jamjoom, K. Choi, A. A. Nahid, and M. A. Samad, “Breast cancer prediction with feature-selected XGB classifier optimized by metaheuristic algorithms,” Journal of Big Data, vol. 12, p. 78, 2025, doi: 10.1186/s40537-025-01132-7.
16. M. A. Kashmoola, M. K. Ahmed, and N. Y. A. Alsaleem, “Network traffic prediction based on boosting learning,” Iraqi Journal of Science, vol. 63, pp. 4047–4056, 2022, doi: 10.24996/ijs.2022.63.9.33.
17. D. Wang, Y. Zhang, and Y. Zhao, “LightGBM: An effective miRNA classification method in breast cancer patients,” in Proc. 2017 Int. Conf. Computational Biology and Bioinformatics, pp. 7–11, 2017, doi: 10.1145/3155077.3155079.
18. T. O. Omotehinwa, D. O. Oyewola, and E. G. Dada, “A light gradient-boosting machine algorithm with tree-structured Parzen estimator for breast cancer diagnosis,” Healthcare Analytics, vol. 4, p. 100218, 2023, doi: 10.1016/j.health.2023.100218.
19. R. H. Ali and W. H. Abdulsalam, “Attention-deficit hyperactivity disorder prediction by artificial intelligence techniques,” Iraqi Journal of Science, 2024, doi: 10.24996/ijs.2024.65.9.39.
20. C. Colak, F. H. Yagin, A. Algarni, A. Algarni, F. Al-Hashem, and L. P. Ardigò, “Proposed comprehensive methodology integrated with explainable artificial intelligence for prediction of possible biomarkers in metabolomics panel of plasma samples for breast cancer detection,” Medicina, vol. 61, p. 581, 2025, doi: 10.3390/medicina61040581.
21. C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A comparative analysis of gradient boosting algorithms,” Artificial Intelligence Review, vol. 54, pp. 1937–1967, 2021, doi: 10.1007/s10462-020-09896-5.
22. M. W. Falah, S. H. Hussein, M. A. Saad, Z. H. Ali, T. H. Tran, R. M. Ghoniem, et al., “Compressive strength prediction using coupled deep learning model with extreme gradient boosting algorithm: Environmentally friendly concrete incorporating recycled aggregate,” Complexity, vol. 2022, p. 5433474, 2022, doi: 10.1155/2022/5433474.
23. K. I. Chibueze, L. I. Ezigbo, and A. Kwubeghari, “Breast cancer prediction with gradient boosting classifiers,” Academy Journal of Science and Engineering, vol. 18, pp. 219–238, 2024.
24. W. H. Abdulsalam, S. Mashhadani, S. S. Hussein, and A. A. Hashim, “Artificial intelligence techniques to identify individuals through palm image recognition,” Computer Science, vol. 20, pp. 165–171, 2025, doi: 10.69793/ijmcs/01.2025/abdulsalam.
25. M. Ban Hassan, W. H. Abdulsalam, Z. Hazim Ibrahim, R. H. Ali, and S. Mashhadani, “Digital intelligence for university students using artificial intelligence techniques,” International Journal of Computing and Digital Systems, vol. 17, pp. 1–10, 2025, doi: 10.12785/ijcds/1571029446.
26. H. Bakhshayesh, S. P. Fitzgibbon, A. S. Janani, T. S. Grummett, and K. J. Pope, “Detecting synchrony in EEG: A comparative study of functional connectivity measures,” Computers in Biology and Medicine, vol. 105, pp. 1–15, 2019, doi: 10.1016/j.compbiomed.2018.12.005.
27. R. K. Ajeena and S. K. Kamal, “Connecting on the lattice based reductions for computing the generators in the ISD method,” Journal of Physics: Conference Series, p. 012060, 2018, doi: 10.1088/1742-6596/1003/1/012060.
28. W. H. Abdulsalam, R. H. Ali, S. H. Jadooa, and S. S. Hussein, “Automated glaucoma detection techniques: A literature review,” Engineering, Technology & Applied Science Research, vol. 15, pp. 19891–19897, 2025, doi: 10.48084/etasr.9316.