Breast Cancer Prognosis using Machine Learning Applications

The maximum deaths worldwide among women is breast cancer (BC). Breast cancer that mainly develops from breast tissues. Due to current population growth in medical research, early diagnosis of cancer has become a critical issue. The likelihood of death is increasing exponentially due to breast cancer as the world's population grows. Breast cancer is the second leading severe cancers that have already been revealed. Cancer prediction therefore plays an important role in updating treatment aspects and survivability standards. As a result of machine learning methods, breast cancer prediction and early detection have been improved significantly, and these methods have been a center of research for this field. Using the Wisconsin Diagnostic Breast Cancer dataset (WBCD), 5 ML algorithms were compared: Decision tree (C4.5), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF) and K-Nearest Neighbour (KNN). Machine learning algorithms are being used to identify the most effective algorithms for early detection and identifying breast cancer in its earliest stages.