Breast cancer classification using machine learning

Abstract

During their life, among 8% of women are diagnosed with Breast cancer (BC), after lung cancer, BC is the second popular cause of death in both developed and undeveloped worlds. BC is characterized by the mutation of genes, constant pain, changes in the size, color(redness), skin texture of breasts. Classification of breast cancer leads pathologists to find a systematic and objective prognostic, generally the most frequent classification is binary (benign cancer/malign cancer). Today, Machine Learning (ML) techniques are being broadly used in the breast cancer classification problem. They provide high classification accuracy and effective diagnostic capabilities. In this paper, we present two different classifiers: Naive Bayes (NB) classifier and knearest neighbor (KNN) for breast cancer classification. We propose a comparison between the two new implementations and evaluate their accuracy using cross validation. Results show that KNN gives the highest accuracy (97.51%) with lowest error rate then NB classifier (96.19 %).