Breast cancer prediction using machine learning

One of the most common cancers is breast cancer that occurs in women and it contributes greatly to the number of deaths that occur worldwide. Breast cancer is caused due to the presence of cancerous lumps inside the breast. A breast lump is a mass that develops in the breast. The lumps can be of various sizes and textures. The lumps found inside the breasts can be either cancerous or non-cancerous. If the lump is cancerous, then no diagnosis needs to be carried out. If the lump is found to be cancerous, then further diagnosis will be carried out to check whether the cancer has affected the rest of the body. The tests that are used for diagnosis are MRI, mammogram, ultrasound, and biopsy. Breast cancer is responsible for death of women from cancer. It is accountable for 16 percent of the overall deaths caused by cancer in the world. In this paper, we are going to predict whether lumps present in the breast are cancerous. To achieve this, we are going to make use of four algorithms which are Support Vector Machines (SVM), K-Nearest Neighbour (KNN). Random Forest and Naïve Bayes. We will compare the efficiency of the machine learning algorithms based on classification metrics and deduce the best one for this research