Abstract
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.
Highlights
Classifying breast cancer can leaddiagnosticians to find anorganized and unbiased prognostic
A Classification algorithm, like Decision Treeare broadly used in the world of medicines to categorize theinformation for diagnosis
The process to identify which set of categories are belongs to one group based on the relevant observations is called as classification method.It comes under supervised machine learning
Summary
Classifying breast cancer can leaddiagnosticians to find anorganized and unbiased prognostic. The limitations are that either they use faulty dataset or they don’t wrangle the data correctly or select features properly. The aim of this very project is to guarantee that the benign and malignant classes of breast cancer are predicted and grouped accurately. Shakkeera L*, School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, India. Rahul Raj Pandey, School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, India. Rahul Bhardwaj, School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, India. Sidhya Virya Singh, School of Computing Science and Engineering, VIT Bhopal University, Madhya Pradesh, India.
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