Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. As the diagnosis of this disease manually takes long hours and the lesser availability of systems, there is a need to develop the automatic diagnosis system for early detection of cancer. In recent years, machine learning has been widely used in detection and achieved favorable performance. In this paper, we have proposed a system that predicts the stages of the breast cancer. Without directly applying the machine learning techniques, we first perform K fold cross validation in the dataset to find which technique is more suitable for this dataset. The Classification and Regression Tress(CART), Linear Support Vector Machines(SVM), Gaussian Naive Bayes(NB) and k-Nearest Neighbors (KNN). techniques are validated and found that SVM is better for the breast cancer dataset. Our proposed model is then constructed using SVM. Using machine learning methods for diagnostic can significantly increase processing speed and on a big scale can make the diagnostic significantly cheaper. Our proposed system has an accuracy of 99.
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