Abstract
During their life, among 8% of women is diagnosed with Breast cancer (BC), after lung cancer, BC is the second popular cause of death in both developed and undeveloped countries. 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 apply five different machine learning algorithms for classifying BC data, and results are visually represented.
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