Breast cancer is the most frequent cancer in women worldwide and is also the most lethal. The reasons for this illness are many and challenging to identify. Furthermore, the diagnostic technique, which determines whether the cancer is benign or malignant, requires substantial work from doctors and physicians. There are many diagnostic tests possible which can be conducted by medical professionals to detect it; however, it has been increasingly strenuous to precisely spot and acts on its prognosis As a result, in recent years, there has been a surge in the use of machine learning and Artificial Intelligence in general as diagnostic tools. ML seeks to make computer selflearning easier. in lieu of contingent on explicit pre-programmed rules and models, it is based on finding patterns in observed data and creating models to predict outcomes and evaluate them on performance measure features like accuracy, precision, and recall. The primary impetus of this review is to culminate all the antecedent studies of machine learning algorithms being utilised for breast cancer prediction. this survey is going to be useful to the researchers because of the elaborated probe of various methodologies for undergoing supplemental inquisitions.

Keywords : Breast Cancer, Medical Diagnosis, Machine Learning, Logistic Regression, KNN, Decision Tree, SVM, Random Forest, Naive Bayes.