https://github.com/asghar-rizvi/health-risk-prediction-platform-with-flask-and-machine-learning
A Health Risk Prediction Platform using Flask and machine learning to predict heart attacks, kidney disease, liver disease, and diabetes. Features a user-friendly interface with HTML, CSS, and JavaScript, along with secure authentication, encrypted passwords, and session management. MySQL is used for database operations, achieving 98% model accurac
https://github.com/asghar-rizvi/health-risk-prediction-platform-with-flask-and-machine-learning
backend css data-science-machine-learning data-science-projects datascience doctor-machine-learning flask frontend html javascript machine-learning python real-world-ml-project webdevelopment website
Last synced: 3 months ago
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A Health Risk Prediction Platform using Flask and machine learning to predict heart attacks, kidney disease, liver disease, and diabetes. Features a user-friendly interface with HTML, CSS, and JavaScript, along with secure authentication, encrypted passwords, and session management. MySQL is used for database operations, achieving 98% model accurac
- Host: GitHub
- URL: https://github.com/asghar-rizvi/health-risk-prediction-platform-with-flask-and-machine-learning
- Owner: asghar-rizvi
- Created: 2024-09-04T22:27:15.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-11-22T15:56:19.000Z (6 months ago)
- Last Synced: 2024-11-22T16:35:50.354Z (6 months ago)
- Topics: backend, css, data-science-machine-learning, data-science-projects, datascience, doctor-machine-learning, flask, frontend, html, javascript, machine-learning, python, real-world-ml-project, webdevelopment, website
- Language: Jupyter Notebook
- Homepage:
- Size: 2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Health Risk Prediction Platform with Flask and Machine Learning
## Project Overview
This project is a full-stack web application that predicts potential health risks, including heart attack risk, kidney disease likelihood, liver disease predictions, and diabetes forecasting. The application combines advanced machine learning models with a sleek and responsive frontend, providing users with accurate health predictions based on their input data.
## Features
- **Home Page**: Introduction to the platform and its health prediction capabilities.
- **User Authentication**: Secure user registration and login using Flask and MySQL.
- **Health Prediction Pages**: Interactive forms for predicting heart attack risk, kidney disease, liver disease, and diabetes.
- **Profile Management**: Users can view and manage their profiles, securely storing their data.
- **High Accuracy Models**: Machine learning models trained on extensive datasets, achieving 98% accuracy.
- **Backend Integration**: Machine learning models are integrated into the Flask backend using Pickle for real-time predictions.
## Technologies Used
- **Frontend**: HTML, CSS, JavaScript
- **Backend**: Flask, Python
- **Machine Learning**: Scikit-learn, Pandas, NumPy
- **Database**: MySQL
- **Model Serialization**: Pickle
## Installation Instructions
1. **Clone the Repository**:
```bash
git clone https://github.com/asghar-rizvi/Health-Risk-Prediction-Platform-with-Flask-and-Machine-Learning.git
2. **Navigate to the Project Directory**:
```bash
cd Health-Risk-Prediction-Platform-with-Flask-and-Machine-Learning
3. **Install Required Dependencies: Ensure you have pip installed and use it to install the necessary packages**:
pip install -r requirements.txt
4. **Set Up the MySQL Database**:
Create a MySQL database and user.
Update the config.py file with your database credentials.
5. **Migrate Database: Run the migration commands to set up the database tables**:
flask db upgrade
6. **Run the Flask Application: Start the development server** :
flask run
## Usage
#### User Registration: Register a new account to access health prediction features.
#### Login: Securely log in to access your profile and prediction tools.
#### Health Predictions: Use the interactive forms on the prediction pages to receive instant health risk assessments.
## Model Information
The platform employs four distinct machine learning models, each trained on health-related datasets. The models are evaluated for accuracy, with the top-performing models achieving up to 98% accuracy.
## Future Enhancements
1. Adding more health prediction models.
2. Implementing real-time data visualization and analytics.
3. Enhancing user interface for improved user experience.
## Contact Information
For any inquiries or support, please reach out to:
Asghar Qamber Rizvi
Email: [email protected]
GitHub: asghar-rizvi