Machine learning techniques for breast cancer diagnosis and treatment: a narrative review

2021 ◽  
Vol 0 ◽  
pp. 0-0
Author(s):  
Masahiro Sugimoto ◽  
Shiori Hikichi ◽  
Masahiro Takada ◽  
Masakazu Toi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Narrative Review ◽  
Diagnosis And Treatment ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Analysis and Prediction of Breast Cancer using Machine Learning Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 26-30
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Breast Cancer Dataset ◽  
Detection Accuracy ◽  
Vast Number ◽  
Learning Techniques ◽  
Number Of Patients

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.

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