Integrated feature selection-based stacking ensemble model using optimized hyperparameters to predict breast cancer with smart web application

• Developed a robust predictive model for breast cancer classification using advanced preprocessing and ensemble learning strategies. • Integrated diverse feature selection methods, enhancing model accuracy and interpretability. • Utilized RandomizedSearchCV to optimize hyperparameters, significantly boosting predictive accuracy. • Created a user-friendly web app for real-time clinical application of the predictive model. Breast cancer is a leading cause of morbidity and mortality among women worldwide, arising from malignant cell transformations in breast tissue. Early detection is paramount as it significantly improves survival rates and reduces the complexity and cost of treatment. Machine learning has revolutionized this field, providing more precise, efficient, and personalized diagnostic methods. Our research aims to develop a robust predictive model for breast cancer classification through rigorous…