Rapid multiplication of cells in the human body
leads to cancer. It is the foremost cause of death due to cancer in
females, after lung cancer. As the breast cancer is one of the
recurrent kinds of cancer, diagnosis of breast cancer recurring is
extremelyessential to increase the survival rate of patient
suffering from it. Although cancer is avertible and also treatable
in primary/early stages yet a vast number of patients are
diagnosed with cancer when it is very late. Almost 8% of females
are detected with breast cancer. Its characteristics are mutation
of genes, constant pain, changes in the size and redness of skin
texture of breasts. With the development of technology and
machine learning techniques, cancer diagnosis and detection
accuracy has greatly improved. This paper presents an outline of
evolved machine learning techniques in this medical field by
applying machine learning algorithms on breast cancer dataset
like Logistic regression, Random Forest, Decision Trees (DT) etc.
BREAST CANCER DIAGNOSIS AND RECURRENCE PREDICTION USING MACHINE LEARNING TECHNIQUES
