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https://github.com/prasadlokhande-880/diseaseprediction
This project involves a disease prediction system where the frontend is developed using React, the backend with Node.js, and a Deep Learning (DL) model for disease prediction.
https://github.com/prasadlokhande-880/diseaseprediction
decision-tree json ml mongodb nodejs react react-hooks react-router
Last synced: about 2 months ago
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This project involves a disease prediction system where the frontend is developed using React, the backend with Node.js, and a Deep Learning (DL) model for disease prediction.
- Host: GitHub
- URL: https://github.com/prasadlokhande-880/diseaseprediction
- Owner: Prasadlokhande-880
- Created: 2024-07-30T14:30:40.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-01T07:28:09.000Z (2 months ago)
- Last Synced: 2024-12-01T08:31:56.719Z (2 months ago)
- Topics: decision-tree, json, ml, mongodb, nodejs, react, react-hooks, react-router
- Language: Jupyter Notebook
- Homepage:
- Size: 1.93 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
---
# Owner Avatar Disease Prediction
This project involves a disease prediction system where the frontend is developed using **React**, the backend with **Node.js**, and a **Deep Learning (DL) model** for disease prediction.
## Table of Contents
1. [Project Overview](#project-overview)
2. [Technologies Used](#technologies-used)
3. [Setup Instructions](#setup-instructions)
4. [Folder Structure](#folder-structure)
5. [How It Works](#how-it-works)
6. [License](#license)
## Project Overview
This application predicts diseases based on the user's input, utilizing a deep learning model trained with historical disease data. The frontend is built with React, allowing users to input their symptoms, while the backend, powered by Node.js, communicates with the model to generate predictions.
### Key Features:
- **Disease Prediction**: User inputs their symptoms, and the system predicts the potential disease.
- **React Frontend**: A user-friendly interface with input fields to collect symptom data.
- **Node.js Backend**: Handles the communication between the frontend and the deep learning model.
- **Deep Learning Model**: A trained model predicts diseases based on the input data.
## Technologies Used
- **Frontend**: React
- **Backend**: Node.js, Express.js
- **Deep Learning Model**: TensorFlow / Keras
- **Database**: (Optional, if you're saving user data for predictions)
- **Other Libraries**: Axios for API calls, dotenv for managing environment variables, etc.
## Setup Instructions
### Prerequisites:
1. Node.js (v16.x or later)
2. Python (v3.x or later) for the deep learning model
3. TensorFlow and Keras for the ML model (can be installed via `pip`)
4. MongoDB or PostgreSQL if you want a database for storing user data (optional)
### Steps to Setup
#### 1. Clone the Repository
```bash
git clone https://github.com/Prasadlokhande-880/Owner-Avatar-Disease-Prediction.git
cd Owner-Avatar-Disease-Prediction
```
#### 2. Set Up the Backend
Navigate to the backend folder and install the required dependencies.
```bash
cd backend
npm install
```
Create a `.env` file to store environment variables like your database credentials, port number, etc.
#### 3. Set Up the Frontend
Navigate to the frontend folder and install the required dependencies.
```bash
cd ../frontend
npm install
```
#### 4. Set Up the ML Model
- Ensure you have Python and TensorFlow installed.
- Place the trained deep learning model in a directory within the backend project.
- If needed, you can load the model in the backend by using the TensorFlow.js or a Python bridge like `flask` to make API calls from Node.js.
#### 5. Run the Backend
Once the backend setup is complete, run the following command to start the Node.js server:
```bash
cd ../backend
npm start
```
The backend will be available at `http://localhost:5000` (default).
#### 6. Run the Frontend
Now, run the React frontend:
```bash
cd ../frontend
npm start
```
The frontend will be available at `http://localhost:3000`.
#### 7. Testing
- Once both the frontend and backend are running, open your browser and navigate to `http://localhost:3000`.
- Input symptoms and hit "Predict". The backend will send the input data to the trained model, and the disease prediction will be displayed on the frontend.
## Folder Structure
```
Owner-Avatar-Disease-Prediction/
│
├── backend/ # Node.js backend code
│ ├── models/ # ML model and related scripts
│ ├── controllers/ # Logic for handling requests
│ ├── routes/ # API routes for disease prediction
│ ├── server.js # Main server file
│ └── .env # Environment variables
│
├── frontend/ # React frontend code
│ ├── src/ # React components
│ ├── public/ # Public assets (images, index.html)
│ └── package.json # Frontend dependencies and scripts
│
└── README.md # This file
```
## How It Works
1. **User Interaction**: The user enters their symptoms into the React-based frontend form.
2. **API Request**: The frontend sends the input data as a request to the backend.
3. **Model Prediction**: The backend processes the input data using a deep learning model trained on disease data to predict the possible disease.
4. **Response**: The predicted disease is returned to the frontend, where it is displayed to the user.
## License
This project is open source and available under the [MIT License](LICENSE).
---
Feel free to adjust the details depending on your specific implementation and model!