Abstract:
Nowadays, Breast cancer is one of the foremost causalities, and it's the second most
familiar reason for death for women. Circulation of distal organ tumors is the primary
cause of death from breast cancer. Breast cancer has now become a common health issue,
and its expansion and harmony have increased recently. Sometimes breast cancer spreads
without a family history. Also, heightened chances of breast cancer retaining aging, genes,
dense breast tissue, obesity, and radiation exposure. Sometimes women don't even know
they have breast cancer. There are two distinct kinds of malignant and benign tumors, and
physicians should use a reliable diagnostic strategy to differentiate between them. The
main ambition of this paper is to utilize the most outcomes in developing a classification
and related strategies. Earlier detection of breast cancer will assist in the survival of
patients with breast cancer. Machine learning helps build planning models to predict
planning models that can be utilized to predict consequences for individual patients. Data
mining and machine learning help in the early detection of breast cancer. The goal of this
study is to review the role of machine learning methods in the prediction and diagnosis of
breast cancer. Most of these methods focus on predicting breast cancer by using machine
learning.
