This is an AI-powered digital health application focused on predicting the risk of Type 2 Diabetes and providing personalized health recommendations to users.
Built as a Flask web application(MVP Version), BeticsAI leverages machine learning to make early predictions based on user input, enabling proactive health management. The application is designed to be lightweight, intuitive, and expandable for integration with smart health devices like wearables in the future.
The goal of BeticsAI is to help individuals and health institutions detect and monitor diabetes risk early, especially in regions where access to regular diagnostics is limited. It empowers users with actionable insights into their health, based on simple, non-invasive data points such as age, BMI, blood pressure, glucose levels, and more.
This project is ideal for students, clinics, NGOs, and developers interested in using AI to solve real-world health problems. BeticsAI includes a clean user dashboard, risk score analysis, and future integration options for recommendation engines and external data sources. The backend is built using Flask and SQLAlchemy, with machine learning models trained using scikit-learn and pandas.
The project is structured to support easy deployment and scalability, with readiness for hosting on platforms like Render, Replit, or Railway. It follows modular design principles with Flask Blueprints for maintainability and allows easy extension of new features such as smart alerts, health report downloads, or chatbot integration.
To run the application locally, clone the repository, install the required dependencies from requirements.txt, and start the Flask server. The app can also be deployed to the web using Render with minimal setup using a Procfile and GitHub integration.
BeticsAI is more than just a prediction tool — it's a vision for affordable, AI-powered preventive care.