Welcome to the PyCoders Health Reporter project! This online tool focuses on building a machine learning-based health problem diagnostics web application. With the power of machine learning algorithms, such as Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Random Forest Classifier, our application aims to predict health problems a person may suffer from based on their health parameters.
The project takes inspiration from the HealthCure project, available at HealthCure Medical Project. We have built upon the concepts and ideas presented in the HealthCure project to create the PyCoders Health Reporter. While we have drawn inspiration from HealthCure, our project is an independent implementation with additional features and improvements.
The PyCoders Health Reporter front-end is made using Flask, a lightweight and efficient web framework in Python. Flask provides a solid foundation for building interactive web applications. We have utilized its features to create a user-friendly interface for our health problem diagnostics tool.
Our project utilizes various machine learning algorithms to achieve accurate predictions. The algorithms employed include:
- Convolutional Neural Networks (CNN)
- Artificial Neural Networks (ANN)
- Random Forest Classifier
By leveraging these algorithms, we strive to provide the highest accuracy possible in predicting three specific diseases: COVID-19, breast cancer, and diabetes. The implementation of these algorithms has been carefully designed to ensure reliable and meaningful results.
This project was developed by the PyCoders group, composed of four talented individuals:
- Neelesh Vashist
- Mukul Bisht
- Rohit Kumar
- Saurabh Singh
Each member has contributed their skills and expertise to different aspects of the project, including front-end development, machine learning algorithm implementation, and overall project management.
To get started with the PyCoders Health Reporter project, follow these steps:
-
Clone the repository to your local machine using the following command:
git clone https://github.com/NeeleshVashist/PyCoders-Health-Reporter.git -
Set up a Python virtual environment to manage the project dependencies. You can use tools like
virtualenvorcondato create a virtual environment. For example, withconda, run the following command:conda create -n health python=3.9 -
Activate the virtual environment. With
conda, run the following command:conda activate health -
Install the required dependencies by running the following command:
pip install opencv-python numpy tensorflow sklearn imutils flask xgboost -
Once the dependencies are installed, navigate to the project directory and run the following command to start the Flask development server:
flask run -
Open your web browser and visit
http://localhost:5000to access the PyCoders Health Reporter application.
We welcome contributions to the PyCoders Health Reporter project. If you have any ideas, bug reports, or feature requests, please submit them via GitHub issues. Additionally, you can fork the repository, make your changes, and submit a pull request for review.
When contributing, please adhere to the following guidelines:
- Fork the repository and create a new branch for your feature or bug fix.
- Ensure your code follows the project's coding style and conventions.
- Write clear commit messages and include a detailed description of the changes you have made.
- Test your changes thoroughly to avoid introducing new bugs.
- Be respectful and considerate of other contributors.
The PyCoders Health Reporter project is released under the MIT License. You can find the full text of the license in the LICENSE file.
If you have any questions or inquiries about the PyCoders Health Reporter project, you can reach out me:
- Neelesh Vashist: LinkedIn
Feel free to get in touch, if you need any further information or have any feedback.
Thank you for using PyCoders Health Reporter! We hope it proves to be a valuable tool for health problem diagnostics.