Machine Learning Application in Healthcare : Breast Cancer Diagnosis and Prognosis

Breast cancer is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. Thus, the correct diagnosis of breast cancer and classification of patients into malignant or benign groups is the subject of much research. The objective of this paper is to review machine learning techniques and their applications in breast cancer diagnosis and prognosis. Because of its unique advantages in critical features detection from complex breast cancer datasets, machine learning is widely recognised as the methodology of choice in breast cancer pattern classification and forecast modelling. This paper provides an overview of machine learning techniques including artificial neural networks, support vector machines, decision trees, and k-nearest neighbours. Then, it investigates their applications in breast cancer. The primary data is drawn from the Wisconsin breast cancer database which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model is also shown