Authors: Ashumati Dahiwadakar
Abstract: Breast cancer (BC) is the most prominent form of cancer among females all over the world. Breast cancer develops from breast cells and is considered a leading cause of death in women. Breast cancer develops from breast cells and is a frequent malignancy in females worldwide. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k- nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques.
