Overview This project aims to provide an accessible and affordable solution for early breast cancer detection, emphasizing its importance in saving lives and raising awareness. The model leverages machine learning techniques to predict the likelihood of breast cancer based on diagnostic input data. It is designed with simplicity and accessibility in mind, making it a valuable tool for individuals, healthcare professionals, and communities.

Features User-Friendly Interface: Easy-to-navigate web interface for inputting data and viewing results. Accurate Predictions: Powered by a machine learning model trained on high-quality datasets. Early Detection Focus: Helps in identifying potential cases at an early stage to improve outcomes. Accessibility: Designed to be cost-effective and usable in resource-limited settings. Technology Stack Backend: Flask (Python) Frontend: Vanilla JavaScript, jQuery, HTML, and CSS Database: [Specify if any, e.g., SQLite or others] Machine Learning: Trained using [model type, e.g., Logistic Regression, Random Forest, or Neural Network] How It Works The user inputs diagnostic parameters such as mean radius, mean texture, mean perimeter, etc. The backend processes the input and passes it to the machine learning model. The model predicts the probability of breast cancer (benign or malignant) based on the input data. Results are displayed in a clear, user-friendly format on the frontend. Dataset The model was trained using the Breast Cancer Wisconsin (Diagnostic) Dataset or another relevant dataset. The dataset contains features computed from digitized images of fine needle aspirate (FNA) of breast mass.