Predictive Modeling for Breast Cancer Diagnosis and Prognosis: A Review

The number of deaths from breast cancer is rising dramatically every year. It is the most common kind of cancer overall and the leading cause of death for women globally. Any advancement in the identification and prognosis of cancer is crucial to a long and healthy life. Therefore, it's critical to have a high level of accuracy in cancer prognosis in order to update patient survivability standards and treatment aspects. Machine learning approaches have shown to be a powerful method, have become a research hotspot, and can significantly contribute to the process of early detection and prediction of breast cancer. In this study, we used the Breast Cancer dataset from kaggle on four machine learning algorithms: Random Forest, Logistic Regression, Decision Tree, and K-Nearest Neighbors (KNN).Among four algorithms, the random forest gives the best accuracy of 96percent.