Breast Cancer Prediction Using Supervised Machine Learning Approach

Abstract

Breast cancer is one of the most prevalent forms of cancer in women worldwide, making early prediction critical to reducing mortality rates. In this study, we propose a machine learning-based approach to predict benign and malignant stages of breast cancer. Our approach utilizes Principal Component Analysis (PCA) for dimensionality reduction and is compared against Random Forest (RF), Logistic Regression (LR), and XGBoost (XGB) machine learning models. The results demonstrate that the proposed approach is highly efficient, accurate, and effective in predicting breast cancer stage. The findings of this study have the potential to revolutionize the medical sector by providing a non-invasive, quick, and cost-effective method for early prediction of breast cancer. The implementation of this approach can lead to improved patient outcomes and reduced healthcare costs.