This project is designed to assist users in predicting potential diseases based on their symptoms. It leverages modern web technologies and machine learning to provide accurate and insightful health predictions.
The system is divided into three main components:
- 🎨 Frontend (React): GitHub Repository - React
- ⚙️ Backend (Node.js): GitHub Repository - Node.js
- 🧠 Machine Learning Model (Flask): GitHub Repository - Flask
The Disease Prediction Project is an intelligent and user-friendly platform that helps users identify potential diseases based on the symptoms they experience. It integrates data processing, machine learning, and an intuitive user interface to deliver reliable and insightful predictions.
The architecture of the Disease Prediction Project is designed to ensure seamless interaction between the user, the frontend, and the backend components. Here's a high-level overview of how the system works:
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User Interaction: The user interacts with the Frontend, developed using React. This interface provides a smooth and intuitive experience for entering symptoms and viewing predictions.
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Frontend to Backend Communication: The React frontend sends the user's symptom data to the Backend, built with Node.js (Express). This backend acts as the central hub of the system, handling requests, processing data, and managing communication with other components.
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Database Interaction: The backend is connected to a MongoDB database, where user information and prediction history are stored. This ensures data persistence and efficient retrieval.
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Machine Learning Prediction: The backend communicates with a Flask API, which hosts the machine learning models. The Flask API processes the symptom data, runs the prediction algorithms, and returns the disease prediction results.
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Prediction Results: Once the prediction is made, the results are sent back to the backend, which then relays them to the frontend. The user sees the prediction results displayed in the React interface.
Below is a diagram illustrating the architecture:
The installation and execution steps for each component (frontend, backend Node.js, and backend Flask) are detailed in the respective README files of their repositories.
🚀 We welcome contributions! If you’d like to enhance this project, please check the contribution guidelines in each repository. You can submit pull requests, report issues, or suggest improvements.
📝 Project developed by Omaima Siaf, Oussama Nouhar and Souhayla Ghanem.
This project is licensed under the MIT License. See the LICENSE file for more details.