Early detection of chronic diseases is crucial for improving patient health outcomes and reducing mortality rates. This project presents an AI-based system for the detection of breast cancer using machine learning techniques. The system utilizes publicly available medical datasets containing clinical and physiological parameters for accurate prediction. Breast cancer detection is performed using features such as cell radius, texture, perimeter, and concavity measurements. Data preprocessing methods such as normalization and feature selection are applied to enhance model performance. Multiple machine learning algorithms, including Decision Tree, Random Forest, XGBoost, are implemented and evaluated to identify the most accurate model. The system generates binary classification results indicating the presence or absence of the disease. A user-friendly web interface is developed using HTML, CSS, and JavaScript, while the backend is implemented using Python with the Django framework for seamless model integration. This project demonstrates the effectiveness of artificial intelligence in improving healthcare diagnostics and supporting clinical decision-making.

Keywords: Artificial Intelligence, Machine Learning, Breast Cancer Detection, Medical Data Analysis, Decision Tree, Random Forest, XGBoost, Flask Framework.