Paper Title
Comparative Analysis of Machine Learning Algorithms to Predict The Onset of Breast Cancer

Breast cancer is an ailment wherein cells in the chest outgrow control. They are of different sorts relying on which cells in the bosom change into a malignant growth. Women are most vulnerable and affected by breast cancer, which if diagnosed early on, the rate of patient survival increases substantially. At present, various ML approaches are utilized to make assets for clinicians which have ended up being a viable component for early identification and analysis of the cancer cells. This paper investigates seven of the most extensively used ML techniques for bosom malignancy ID and conclusion at starting stages utilizing the WDBC dataset. Each strategy has been evaluated for its accuracy, specificity, precision, recall, F-measure, and ROC. This paper’s results provide an overview of emerging machine learning approaches for malignant growth determination in the breast. Keywords - Machine Learning, Cancer Dataset, Random Forest, Support Vector Machine