Abstract: Science Set Journal of Cancer Research

Abstract:
The number of deaths due to breast cancer is rapidly increasing annually, making it a typical cancer type and primary cause of mortality in females worldwide. Advances in predicting and detecting cancer are crucial for maintaining good health and improving patient care and survival rates. Therefore, achieving a high accuracy rate in cancer prediction is essential for updating the treatment protocols and improving the patient's survival rates. Machine learning approaches have become an important research focus in this area, showing effectiveness in predicting and detecting breast cancer. This study used the K Nearest Neighbor (KNN), Xtreme Gradient Boosting (XGB), and Support Vector Machine (SVM) methods for the classification of cancer patients. The experimental results show that the SVM and XGB models have better prediction results.