A Survey on Machine Learning Techniques to Detect Breast Cancer

One of the diseases that most commonly affect women worldwide is breast cancer. In 2015, 8.8 million people lost their lives to cancer. Currently, in terms of incidence, lung cancer is second only to breast cancer among women worldwide. Cancer patients' lives can be saved through early detection and diagnosis. Therefore, it's critical to identify the tumor as soon as feasible. This survey covered several studies that were utilized to pinpoint this breast cancer as well as the methods they employed. This research study also discusses about the accuracy of different strategies and the datasets that were used and finally gives an overview of machine learning methods for recognizing and categorizing breast cancer. Research has been conducted on a variety of techniques, including Support Vector Machines (SVMs), Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, Decision Tree, Neural Network, Decision Support System, K-Fold Cross Validation, etc. Finally, all the techniques are compared based on their merits and shortcomings. This research study aims to gather and assess different breast cancer detection methods.