Breast Cancer Detection And Classification Using Machine Learning

Abstract

Breast cancer is one of the most prevalent and life-threatening diseases affecting women globally. Early detection and
accurate classification are crucial for improving survival rates and reducing mortality. This research paper presents
a machine learning-based approach for the detection and classification of breast cancer tumors into benign and
malignant categories. The proposed system utilizes supervised learning algorithms such as Logistic Regression,
Support Vector Machine (SVM), Decision Trees, and Random Forest to analyze medical data. The system is trained
and evaluated using the Wisconsin Breast Cancer Dataset, which contains features extracted from digitized images
of fine needle aspirates of breast masses. Data preprocessing techniques such as normalization, feature scaling, and
handling missing values are applied to improve model performance. The results indicate that ensemble models like
Random Forest achieve higher accuracy compared to traditional classifiers. The proposed system aims to assist
healthcare professionals by providing a reliable and efficient diagnostic tool, thereby reducing human error and
enabling early-stage detection of breast cancer.