Breast Cancer: Aetiology, Diagnosis, Prevention, Treatment, and the Transformative Role of Artificial Intelligence
DOI:
https://doi.org/10.63503/j.ijssic.2025.207Keywords:
Breast cancer, Aetiology, Diagnosis, Treatment, Prevention, Deep Learning, Explainable AIAbstract
Breast cancer is a life-threatening disease affecting women, and it needs effective and efficient diagnostic procedures to detect it early enough to ensure effective management. Artificial Intelligence (AI) offers significant promise for improving breast cancer detection and treatment; however, its application remains constrained by methodological, ethical, and infrastructural limitations. This paper examines a hybrid anomaly-detection model applied to the breast cancer dataset from Kaggle. The dataset is preprocessed and class-balanced using Mean Absolute Error (MAE) thresholding and SMOTE techniques. Twelve ML and DL models were trained and compared using the dataset in terms of typical evaluation metrics. The highest diagnostic performance was obtained with the LightGBM classifier with 0.9912 Accuracy, 0.9880 F1 Score, and a Specificity (1.0000). The Results indicate the usefulness of ensemble techniques in high-accuracy biomedical anomaly detection. Persistent issues with insufficient dataset diversity, model interpretability, and clinical standards should be resolved to facilitate the deployment of reliable AI systems in the fields of breast cancer detection, prevention, and therapy.
References
[1] Z. Mirza, M. S. Ansari, M. S. Iqbal, N. Ahmad, N. Alganmi, H. Banjar, M. H. Al-Qahtani, and S. Karim, “Identification of novel diagnostic and prognostic gene signature biomarkers for breast cancer using artificial intelligence and machine learning assisted transcriptomics analysis,” Cancers, vol. 15, Art. no. 3237, 2023, doi: 10.3390/cancers15123237.
[2] C. Katsura, I. Ogunmwonyi, H. K. N. Kankam, and S. Saha, “Breast cancer: Presentation, investigation and management,” British Journal of Hospital Medicine, 2022, doi: 10.12968/hmed.2021.0459.
[3] N. H. Singh, U. Köse, and S. P. Gochhayat, Eds., Deep Learning in Biomedical Signal and Medical Imaging. Boca Raton, FL, USA: CRC Press, Taylor & Francis, 2023.
[4] H. Xiao, Y. Zou, J. Wang, and S. Wan, “A review of artificial intelligence-based protein subcellular localization,” Biomolecules, vol. 14, Art. no. 409, 2024, doi: 10.3390/biom14040409.
[5] J. Wang, Z. Zhang, and Y. Wang, “Utilizing feature selection techniques for AI-driven tumor subtype classification,” Biomolecules, vol. 15, Art. no. 81, 2025, doi: 10.3390/biom15010081.
[6] J. U. Kazi, Python Essentials for Biomedical Data Analysis. Cham, Switzerland: Springer Nature, 2025, doi: 10.1007/978-3-031-85600-6.
[7] X. Luo, J. Y. Chen, M. Ataei, and A. Lee, “Microfluidic compartmentalization platforms for single-cell analysis,” Biosensors, vol. 12, Art. no. 58, 2022, doi: 10.3390/bios12020058.
[8] C. Hayford et al., “Microfluidics-free single-cell genomics with templated emulsification,” Nature Biotechnology, vol. 41, pp. 1557–1566, Nov. 2023, doi: 10.1038/s41587-023-01685-z.
[9] W. M. Zhou et al., “Microfluidics applications for high-throughput single-cell sequencing,” Journal of Nanobiotechnology, vol. 19, Art. no. 312, 2021, doi: 10.1186/s12951-021-01045-6.
[10] R. Nema, A. Kumar, and D. K. Saini, Eds., Advances in Cancer Detection, Prediction, and Prognosis Using Artificial Intelligence and Machine Learning. Singapore: Springer Nature, 2025, doi: 10.1007/978-981-96-9346-7.
[11] M. Ossandon, B. Prickril, and A. Rasooly, Eds., Cancer Detection and Diagnosis: A Handbook of Emerging Technologies. Boca Raton, FL, USA: CRC Press, 2025, doi: 10.1201/9781003449942.
[12] T. T. Ogunjobi et al., “Bioinformatics applications in chronic diseases,” Medinformatics, vol. 1, no. 1, pp. 1–18, 2024, doi: 10.47852/bonviewMEDIN42022335.
[13] J. Lötsch, D. Kringel, and A. Ultsch, “Explainable artificial intelligence in biomedicine,” BioMedInformatics, vol. 2, pp. 1–17, 2022, doi: 10.3390/biomedinformatics2010001.
[14] T. Hulsen, “Literature analysis of artificial intelligence in biomedicine,” Annals of Translational Medicine, vol. 10, no. 23, Art. no. 1284, 2022, doi: 10.21037/atm-2022-50.
[15] M. Liu, G. Srivastava, J. Ramanujam, and M. Brylinski, “SynerGNet: A graph neural network model to predict anticancer drug synergy,” Biomolecules, vol. 14, Art. no. 253, 2024, doi: 10.3390/biom14030253.
[16] N. Q. K. Le, “Redefining biomedicine: Artificial intelligence at the forefront of discovery,” Biomolecules, vol. 14, Art. no. 1597, 2024, doi: 10.3390/biom14121597.
[17] X. Wang, L. Yang, and R. Wang, “mRCat: A CatBoost predictor for mRNA subcellular localization,” Biomolecules, vol. 14, Art. no. 767, 2024, doi: 10.3390/biom14070767.
[18] Z. Zhang, R. Zhang, K. Xiao, and X. Sun, “G4Beacon: An in vivo G4 prediction method,” Biomolecules, vol. 13, Art. no. 292, 2023, doi: 10.3390/biom13020292.
[19] F. Dilnawaz and A. K. Behura, Artificial Intelligence-Based Cancer Nanomedicine. Sharjah, UAE: Bentham Science, 2022, doi: 10.2174/97898150505611220101.
[20] A. Mitsala et al., “Artificial intelligence in colorectal cancer screening, diagnosis and treatment,” Current Oncology, vol. 28, no. 3, pp. 1581–1607, 2021, doi: 10.3390/curroncol28030149.
[21] I. N. Weerarathna, A. R. Kamble, and A. Luharia, “Artificial intelligence applications for biomedical cancer research,” Cureus, vol. 15, no. 11, Art. no. e48307, 2023, doi: 10.7759/cureus.48307.
[22] Z. Dlamini et al., “Artificial intelligence and big data in cancer and precision oncology,” Computational and Structural Biotechnology Journal, vol. 18, pp. 2300–2311, 2020, doi: 10.1016/j.csbj.2020.08.019.
[23] D. Zheng, X. He, and J. Jing, “Overview of artificial intelligence in breast cancer medical imaging,” Journal of Clinical Medicine, vol. 12, Art. no. 419, 2023, doi: 10.3390/jcm12020419.
[24] S. B. Johnson et al., “Using ChatGPT to evaluate cancer myths and misconceptions,” JNCI Cancer Spectrum, vol. 7, no. 2, Art. no. pkad015, 2023, doi: 10.1093/jncics/pkad015.
[25] Z. H. Chen et al., “Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine,” Cancer Communications, vol. 41, no. 11, pp. 1100–1115, 2021, doi: 10.1002/cac2.12215.
[26] J. Liao et al., “Artificial intelligence assists precision medicine in cancer treatment,” Frontiers in Oncology, vol. 12, Art. no. 998222, 2023, doi: 10.3389/fonc.2022.998222.
[27] M. J. Iqbal et al., “Clinical applications of artificial intelligence and machine learning in cancer diagnosis,” Cancer Cell International, vol. 21, Art. no. 270, 2021, doi: 10.1186/s12935-021-01981-1.
[28] A. M. Sebastian and D. Peter, “Artificial intelligence in cancer research: Trends, challenges and future directions,” Life, vol. 12, Art. no. 1991, 2022, doi: 10.3390/life12121991.
[29] D. M. Koh et al., “Artificial intelligence and machine learning in cancer imaging,” Communications Medicine, 2022, doi: 10.1038/s43856-022-00199-0.
[30] B. Hunter, S. Hindocha, and R. W. Lee, “The role of artificial intelligence in early cancer diagnosis,” Cancers, vol. 14, Art. no. 1524, 2022, doi: 10.3390/cancers14061524.
[31] Y. V. Pathak, S. Saikia, S. Pathak, J. Patel, and B. G. Prajapati, Eds., Artificial Intelligence in Bioinformatics and Chemoinformatics. Boca Raton, FL, USA: CRC Press, 2024.
[32] P. Gentile, “Breast cancer therapy: The potential role of mesenchymal stem cells,” Biomedicines, vol. 10, Art. no. 1179, 2022, doi: 10.3390/biomedicines10051179.
[33] J. Dombi and O. Csiszár, Explainable Neural Networks Based on Fuzzy Logic and Multi-Criteria Decision Tools. Cham, Switzerland: Springer Nature, 2021, doi: 10.1007/978-3-030-72280-7.
[34] S. Wang et al., “A review of 3D printing technology in pharmaceutics,” Pharmaceutics, vol. 15, Art. no. 416, 2023, doi: 10.3390/pharmaceutics15020416.
[35] S. M. Badr-Eldin et al., “Three-dimensional in vitro cell culture models for efficient drug discovery,” Pharmaceuticals, vol. 15, Art. no. 926, 2022, doi: 10.3390/ph15080926.
[36] T. Antao, Bioinformatics with Python Cookbook, 3rd ed. Birmingham, U.K.: Packt Publishing, 2022.
[37] M. Domb, S. Joshi, and A. Khan, “Anomaly detection in IoT,” in Artificial Intelligence, IntechOpen, 2024, doi: 10.5772/intechopen.111944.
[38] D. Tarin, Understanding Cancer: The Molecular Mechanisms, Biology, Pathology and Clinical Implications. Cham, Switzerland: Springer Nature, 2023, doi: 10.1007/978-3-030-97393-3.
[39] Z. Zhang et al., “Protein language models learn evolutionary statistics of interacting sequence motifs,” Proceedings of the National Academy of Sciences, vol. 121, no. 45, Art. no. e2406285121, 2024, doi: 10.1073/pnas.2406285121.
[40] C. Wang et al., “Microfluidic biochips for single-cell analysis of multiomics,” Advanced Science, vol. 11, no. 28, Art. no. e2401263, 2024, doi: 10.1002/advs.202401263.
[41] Y. You et al., “Artificial intelligence in cancer target identification and drug discovery,” Signal Transduction and Targeted Therapy, vol. 7, Art. no. 156, 2022, doi: 10.1038/s41392-022-00994-0.
[42] A. Saini et al., “Cancer causes and treatments,” International Journal of Pharmaceutical Sciences and Research, vol. 11, no. 7, pp. 3121–3134, 2020, doi: 10.13040/IJPSR.0975-8232.11(7).3121-34.
[43] K. P. T. Kathryn and S. E. H. Cokenakes, “Breast cancer treatment,” American Family Physician, vol. 104, no. 2, Aug. 2021.
[44] P. K. Das, H. K. Tripathy, and S. A. M. Yusof, Eds., Privacy and Security Issues in Big Data. Singapore: Springer Nature, 2021, doi: 10.1007/978-981-16-1007-3.
[45] A. F. M. Gavriilidou et al., “High-throughput native mass spectrometry screening in drug discovery,” Frontiers in Molecular Biosciences, vol. 9, Art. no. 837901, 2022, doi: 10.3389/fmolb.2022.837901.
[46] J. Baker-Brunnbauer, Trustworthy Artificial Intelligence Implementation. Cham, Switzerland: Springer, 2023, doi: 10.1007/978-3-031-18275-4.
[47] N. J. Ayon, “High-throughput screening for antibacterial drug discovery,” Metabolites, vol. 13, Art. no. 625, 2023, doi: 10.3390/metabo13050625.
[48] F. A. Batarseh and L. J. Freeman, AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI. London, U.K.: Academic Press, Elsevier, 2023.
[49] B. Ammanath, Trustworthy AI: A Business Guide. Hoboken, NJ, USA: Wiley, 2022.
[50] A. Dingli and D. Farrugia, Neuro-Symbolic AI. Birmingham, U.K.: Packt Publishing, 2023.
[51] L. Alzubaidi et al., “Towards risk-free trustworthy artificial intelligence,” International Journal of Intelligent Systems, vol. 2023, Art. no. 4459198, 2023, doi: 10.1155/2023/4459198.
[52] R. W. McGee, “Using Chinese herbal medicine to treat cancer patients,” Biomedical Journal of Scientific & Technical Research, vol. 56, no. 5, 2024.
[53] D. Chen et al., “Integrated machine learning and bioinformatics analyses for cancer prognosis,” International Journal of Biological Sciences, vol. 18, no. 1, pp. 360–373, 2022, doi: 10.7150/ijbs.66913.
[54] G. Liang et al., “The emerging roles of artificial intelligence in cancer drug development,” Biomedicine & Pharmacotherapy, vol. 128, Art. no. 110255, 2020, doi: 10.1016/j.biopha.2020.110255.
[55] G. Dileep and S. G. Gianchandani Gyani, “Artificial intelligence in breast cancer screening,” Cureus, vol. 14, no. 10, Art. no. e30318, 2022, doi: 10.7759/cureus.30318.
[56] X. Hou et al., “Artificial intelligence in cervical cancer screening,” Frontiers in Oncology, vol. 12, Art. no. 851367, 2022, doi: 10.3389/fonc.2022.851367.
[57] F. Chollet, Deep Learning with Python, 2nd ed. Shelter Island, NY, USA: Manning Publications, 2021.
[58] W. Liu et al., Graph Neural Network Methods and Applications in Scene Understanding. Singapore: Springer Nature, 2024, doi: 10.1007/978-981-97-9933-6.
[59] L. Gianfagna and A. Di Cecco, Explainable AI with Python. Cham, Switzerland: Springer Nature, 2021, doi: 10.1007/978-3-030-68640-6.
[60] A. Munir, J. Kong, and M. A. Qureshi, Accelerators for Convolutional Neural Networks. Hoboken, NJ, USA: Wiley, 2024.
[61] S. K. Niazi and Z. Mariam, “Computer-aided drug design and drug discovery,” Pharmaceuticals, vol. 17, Art. no. 22, 2024, doi: 10.3390/ph17010022.
[62] A. K. N. Neelam, “Advancing drug discovery through computer-aided design,” Discover Pharmaceutical Sciences, vol. 1, Art. no. 8, 2025, doi: 10.1007/s44395-025-00008-2.
[63] A. B. Gurung et al., “An updated review of computer-aided drug design,” BioMed Research International, vol. 2021, Art. no. 8853056, 2021, doi: 10.1155/2021/8853056.
[64] L. K. Vora et al., “Artificial intelligence in pharmaceutical technology,” Pharmaceutics, vol. 15, Art. no. 1916, 2023, doi: 10.3390/pharmaceutics15071916.
[65] S. Nasim et al., “A novel approach for PCOS prediction using machine learning,” IEEE Access, vol. 10, pp. 97610–97624, 2022, doi: 10.1109/ACCESS.2022.3205587.
[66] N. Goel and R. Kumar Yadav, Eds., Internet of Things Enabled Machine Learning for Biomedical Applications. Boca Raton, FL, USA: CRC Press, 2025.
[67] J. Zyla et al., “Combining radiomics and metabolomics for lung cancer diagnosis,” Biomolecules, vol. 14, Art. no. 44, 2024, doi: 10.3390/biom14010044.
[68] S. M. Khade and R. G. Mishra, Eds., Future of AI in Biomedicine and Biotechnology. Hershey, PA, USA: IGI Global, 2024.
[69] S. K. Jha et al., Eds., Computational Advances in Bio and Medical Sciences. Cham, Switzerland: Springer Nature, 2021, doi: 10.1007/978-3-030-79290-9.
[70] H. S. Madhusudhan et al., Eds., Artificial Intelligence and Cloud Computing Applications in Biomedical Engineering. Boca Raton, FL, USA: CRC Press, 2025.
[71] C. Cerchia and A. Lavecchia, “New avenues in AI-assisted drug discovery,” Drug Discovery Today, vol. 28, no. 4, Apr. 2023.
[72] A. S. Walker and J. Clardy, “A machine learning bioinformatics method to predict biological activity,” Journal of Chemical Information and Modeling, doi: 10.1021/acs.jcim.0c01304.
[73] S. Srivastava et al., Bio-Inspired Optimization for Medical Data Mining. Beverly, MA, USA: Scrivener Publishing, 2024.
[74] W. Xie et al., “Transformer-based multimodal data fusion for COPD classification,” Biomolecules, vol. 13, Art. no. 1391, 2023, doi: 10.3390/biom13091391.
[75] K. Athanasopoulou et al., “Artificial intelligence: The milestone in modern biomedical research,” BioMedInformatics, vol. 2, pp. 727–744, 2022, doi: 10.3390/biomedinformatics2040049.
[76] A. R. Khan and T. Saba, Eds., Explainable Artificial Intelligence in Medical Imaging. Boca Raton, FL, USA: Auerbach Publications, 2025.
[77] S. P. Yadav, S. Yadav, and V. H. C. de Albuquerque, Eds., Advances in Fuzzy-Based Internet of Medical Things. Beverly, MA, USA: Scrivener Publishing, 2024.
[78] A. Gogoi and N. Mazumder, Eds., Biological and Medical Physics, Biomedical Engineering. Singapore: Springer Nature, 2024, doi: 10.1007/978-981-97-5345-1.
[79] A. Mukhopadhyay et al., Multiobjective Optimization Algorithms for Bioinformatics. Singapore: Springer Nature, 2024, doi: 10.1007/978-981-97-1631-9.
[80] L. Wang, “Mammography with deep learning for breast cancer detection,” Frontiers in Oncology, vol. 14, Art. no. 1281922, 2024, doi: 10.3389/fonc.2024.1281922.
[81] R. Buyya et al., Security and Privacy Issues in Internet of Medical Things. Cambridge, MA, USA: Elsevier Academic Press, 2023.
[82] M. Torrente et al., “An AI-based tool for prognosis in cancer patients,” Cancers, vol. 14, Art. no. 4041, 2022, doi: 10.3390/cancers14164041.
[83] W. Y. Lee et al., “Machine learning for recommending herbal formulae,” Biomolecules, vol. 12, Art. no. 1604, 2022, doi: 10.3390/biom12111604.
[84] X. Wang, L. Yang, and R. Wang, “DRpred: A deep learning-based predictor for multi-label mRNA subcellular localization,” Biomolecules, vol. 14, Art. no. 1067, 2024, doi: 10.3390/biom14091067.
[85] S. N. Shivhare and N. Kumar, “Brain tumor detection using manifold ranking in FLAIR MRI,” in Proc. 2019 Int. Conf. Emerging Trends in Information Technology (ICETIT), Lecture Notes in Electrical Engineering, vol. 605, P. Singh, B. Panigrahi, N. Suryadevara, S. Sharma, and A. Singh, Eds. Cham, Switzerland: Springer, 2020, pp. 271–279, doi: 10.1007/978-3-030-30577-2_25.
