Neural Network Approaches for Early Breast Cancer Detection

Abstract

Breast cancer research remains essential due to its high mortality rates and the critical need for improved diagnostic accuracy. This study investigates the application of neural network techniques to enhance early breast cancer detection, utilizing Artificial Neural Networks (ANN) implemented in MATLAB. By analyzing datasets from the UCI Machine Learning Repository, specifically the Coimbra and Wisconsin Breast Cancer datasets, this research demonstrates the accuracy and efficiency of neural networks in classifying malignant and benign cases. Methodologically, the study involves data preprocessing, ANN pattern recognition modeling, and testing across multiple metrics including confusion matrices and ROC plots to validate the model's predictive performance. The findings underscore the potential of neural networks as a diagnostic support tool, facilitating faster and more accurate cancer detection, thereby contributing to improved patient outcomes and supporting global health initiatives for early diagnosis.