ABSTRACT
Advancements in machine learning and data analytics have transformed the landscape of biomedical decision-making, offering innovative solutions to address the complexities of diagnosing, prognosing, and planning treatments for breast cancer. Breast cancer is a significant health concern, necessitates precise and timely decision-making in diagnosis, prognosis, and treatment. This research endeavours to advance decision-making in breast cancer care through the application of cutting-edge machine learning and data analytics techniques. The context of this research is the urgent need for improved breast cancer management. Current approaches, while invaluable, face inherent complexities in handling diverse data sources and tailoring treatments to individual patients. Advanced machine learning and data analytics offer the potential to mitigate these challenges. This paper provides a comprehensive examination of the application of machine learning and data analytics in the realm of breast cancer. We begin by delving into the sources of biomedical data and their pre-processing, subsequently exploring a range of machine learning algorithms and feature engineering methods. Our primary objectives are to develop highly accurate diagnostic models for breast cancer, to predict disease progression through advanced prognosis models. This research also underscores the importance of model interpretability and ethical considerations, promoting transparency and equity in the application of artificial intelligence in clinical practice. Our findings reveal the potential for advanced machine learning and data analytics to significantly enhance decision-making in breast cancer care.
