Machine Learning Techniques for Improved Breast Cancer Detection and Prognosis—A Comparative Analysis

Breast cancer prevails as the most widespread and second deadliest cancer in women. The typical symptoms of breast cancer are minimal and curable when the tumor is small, hence screening is crucial for timely detection. Since delayed diagnosis contributes to considerable number of deaths, a number of cross-disciplinary techniques have been introduced in medical sciences to aid healthcare experts in the swift detection of breast cancer. A vast amount of data is collected in the process of breast cancer detection and therapy through consultation reports, histopathological images, blood test reports, mammography results etc. This data can generate highly powerful prediction models, if properly used that can serve as a support system to assist doctors in the early breast cancer diagnosis and prognosis. This paper discusses the need of machine learning for breast cancer detection, presents a systematic review of recent and notable works for precise detection of breast cancer, followed by a comparative analysis of the machine learning models covered in these studies. Then, we have performed breast cancer detection and prognosis on three benchmark datasets of Wisconsin, using seven popular machine learning techniques, and noted the findings. In our experimental setup, K-Nearest Neighbor and Random Forest perform with the highest accuracy of 97.14% over Wisconsin Breast Cancer Dataset (original). Moreover, random forest displays best performance over all the three datasets. Finally, the paper summarizes the challenges faced together with conclusions drawn and prospective scope of machine learning in breast cancer detection and prognosis.