Breast Cancer Detection Using Machine Learning

Breast cancer is the most prevalent cancer among women worldwide and early diagnosis is crucial for increasing survival rates. This research proposes a machine learning-based approach for the classification of breast cancer as benign or malignant using the Wisconsin Breast Cancer Dataset (WBCD). The Random Forest Classifier was selected for its robustness and accuracy in handling structured data. The trained model was deployed through a user-friendly Python GUI built with Tkinter, enabling real-time predictions. The system achieved an accuracy of over 96%, demonstrating its potential as a low-cost, accessible, and reliable decision support tool in breast cancer screening and diagnosis.