Abstract

Abstract:- Breast cancer is a type of cancer that develops in the cells of the breast and is a highly prevalent cancer in women.Breast cancer, like lung cancer, is a life-threatening condition for women. In this paper, a Support Vector Method (SVM) isdeveloped to improve the automatic detection of breast cancer. Cancer is the world's second leading cause of death. Earlyidentification of breast cancer has been the subject of a variety of studies in order to begin treatment and boost the chances ofsurvival. The majority of the research focused on mammography images. However, mammography scans can sometimes resultin false positives, putting the patient's health at risk. Alternative methods that are easier to adopt and work with different datasets, as much as cheaper and safer, are needed to create more dependable predictions. For effective breast cancer detection,this research offers a hybrid model combining numerous Machine Learning (ML) methods, including Convolutional NeuralNetworks (CNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and DecisionTree (DT). The datasets utilised for breast cancer detection and diagnosis connected to the application are also discussed inthis paper.Keywords: Breast Cancer detection, Machine Learning Algorithm, Risk Factor, Mammography images