Abstract
Machine learning is frequently used in medical applications such as detection of the type of cancerous cells. Breast cancer represents one of the diseases that causes a high number of deaths every year. It is the most common type of cancer and the main cause of women's deaths worldwide. The cancerous cells are classified as Benign (B) or Malignant (M). There are many algorithms for classification and prediction of breast cancer: Support Vector Machine (SVM), Decision Tree (CART), Naive Bayes (NB) and k Nearest Neighbours (kNN). In this project, Support Vector Machine (SVM) on the Wisconsin Breast Cancer dataset is used. The dataset is also trained with the other algorithms: KNN, Naives Bayes and CART and the accuracy of prediction for each algorithm is compared.
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