MATLAB Application for Breast Cancer Detection with Clinical Dataset

This article presents a MATLAB-based application designed to assist medical professionals in breast cancer detection. The system employs machine learning algorithms trained and validated using comprehensive clinical datasets packaged in .names (feature description) and .data (case data) files containing over 200 annotated medical cases. The implementation includes data preprocessing modules for handling medical imaging data, feature extraction routines for identifying pathological patterns, and classification algorithms (such as SVM or neural networks) for malignancy prediction. Through rigorous testing with cross-validation techniques, the application achieves high diagnostic accuracy, enabling physicians to make more precise clinical decisions. This tool not only enhances diagnostic reliability but also contributes to improved patient survival rates and quality of life. By demonstrating the practical integration of computational methods in medical diagnostics, this project aims to advance healthcare technology and foster innovation in medical AI applications.