Abstract

Breast cancer is one of the leading causes of death among women worldwide. Early detection plays a crucial role in improving survival rates, and machine learning (ML) provides powerful tools for identifying cancerous tumors in medical imaging and diagnostic data. This paper explores various machine learning techniques used for breast cancer detection, with a particular focus on the Wisconsin Breast Cancer Dataset (WBCD). We evaluate several classification models, including Logistic Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random Forest (RF), for their ability to accurately classify benign and malignant tumors. The goal of this paper is to demonstrate the use of supervised learning techniques to enhance early breast cancer detection.