Advancing Breast Cancer Diagnosis with Machine Learning: Exploring Data Balancing, Feature Selection, and Bayesian Optimization

Breast cancer represents the preeminent widespread type of cancer worldwide among women. The World Health Organization estimates that an annual total of 2.3 million new breast cancer cases are recorded. Also, breast cancer stands as the top cause of cancer mortality in the female population, claiming more than 685,000 lives by 2020. In response to the alarming spread of breast cancer and its significant impact on women's health, it has become imperative to develop innovative techniques and methods for early detection, accurate diagnosis, and effective treatment. In this perspective, the current paper suggests a comparison of several machine learning methods enhanced with data balancing, feature selection, and hyperparameter-tuning Bayesian search strategies. The dataset employed is an unbalanced set of 569 entries comprising 31 medical features associated with breast cancer. With machine learning, data balancing, feature selection, and hyperparameter optimization methods, we can make significant strides in improving the accuracy of breast cancer classification and prediction techniques. All models in our study demonstrated promising performances, exceeding 98% across all classification metrics for some of them, which will improve breast cancer diagnosis and treatment systems and offer healthcare professionals more practical resources.