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The Projects Features Detection of Breast Cancer Using Machine Learning. It has been tested that while there exists several machine learning models,Support Vector Machine or SVM in short is reported to have highest accuracy of (approximately 97%) in detecting breast cancer.

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Breast Cancer detection in ML

This program utilizes machine learning to predict if a tumor is malignant or benign using the Breast Cancer Wisconsin dataset.

Role Of Machine Learning In Detection Of Breast Cancer

A mammogram is an x-ray picture of the breast. It can be used to check for breast cancer in women who have no signs or symptoms of the disease. It can also be used if you have a lump or other sign of breast cancer.

Screening mammography is the type of mammogram that checks you when you have no symptoms. It can help reduce the number of deaths from breast cancer among women ages 40 to 70. But it can also have drawbacks. Mammograms can sometimes find something that looks abnormal but isn't cancer. This leads to further testing and can cause you anxiety. Sometimes mammograms can miss cancer when it is there. It also exposes you to radiation. You should talk to your doctor about the benefits and drawbacks of mammograms. Together, you can decide when to start and how often to have a mammogram.

Results

An accuracy of 96% was achieved by using SVM model and after normalization technique after optimisation of C and Gamma parameters it was increased to a value of a 97%.

Link to dataset:

https://www.kaggle.com/uciml/breast-cancer-wisconsin-data

contact

Email : [email protected]

Linkedin : https://www.linkedin.com/in/raman-kumar-59182620a/

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The Projects Features Detection of Breast Cancer Using Machine Learning. It has been tested that while there exists several machine learning models,Support Vector Machine or SVM in short is reported to have highest accuracy of (approximately 97%) in detecting breast cancer.

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