“Deep Learning-Driven Risk Factor Stratification in Breast Cancer”

Breast cancer poses a significant health challenge for women worldwide, with early and precise risk assessment of metastasis being critical for effective treatment. Traditional evaluation methods often rely on subjective judgments and extensive manual processes. This study introduces an advanced deep neural network (DNN) model enhanced with L2 regularization to categorize breast cancer There are four types of risk: High Risk, Moderate Risk, Low Risk, and Very High Risk. Utilizing ReLU activation functions in its hidden layers, dropout for regularization, and batch normalization for stability, the model achieves a notable classification accuracy of 92% on a comprehensive Kaggle dataset with varied clinical and demographic features. This study is essential because it shows how deep learning methods can be used to automate and refine risk stratification, offering a robust tool for clinicians to personalize treatment plans. The innovation of this model lies in its integration of advanced regularization methods and impressive classification accuracy, marking a significant advancement in automated breast cancer risk assessment.