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Multiple Disease Prediction using Streamlit

This project aims to predict multiple diseases including diabetes, heart disease, and Parkinson's disease using Support Vector Machines (SVM) and logistic regression models. The prediction tool is implemented as a Streamlit web application.

Introduction

Healthcare is an area where predictive modeling can significantly contribute to early diagnosis and treatment of diseases. This project utilizes machine learning techniques to predict the likelihood of certain diseases based on input features such as medical history, demographic information, and physiological parameters.

Technologies Used

  • Python
  • Streamlit
  • Scikit-learn
  • Jupyter Notebook
  • Anaconda (Spyder)

Models Used

  • Support Vector Machines (SVM)
  • Logistic Regression

Deployment

The application is deployed using Streamlit Cloud, making it accessible via a web browser. Users can interact with the application and input their data to obtain predictions for the aforementioned diseases.

Instructions

To run this application locally, follow these steps:

  1. Clone the repository to your local machine.
  2. Ensure you have Python and Anaconda installed.
  3. Install the required packages by running pip install -r requirements.txt.
  4. Open Spyder from Anaconda Navigator or command line.
  5. Open the Streamlit app file (Multiple_disease.py) in Spyder.
  6. Run the Streamlit app.
  7. Access the app via the provided local URL.

Files Included

  • Multiple_disease.py: Streamlit application code.
  • model_training/: Directory containing Jupyter Notebook code for model training.
  • requirements.txt: List of required Python packages.
  • datasets/: Directory containing datasets used for model training.

License

This project is licensed under the BSD-2 License.

Contributor

  • Mudit Golchha

For any inquiries or feedback, feel free to contact the contributor via email at golchhamudit2203@gmail.com.

Disclaimer: This application is for educational and informational purposes only. It is not intended to provide medical advice or diagnoses. Always consult with a qualified healthcare professional for medical concerns and treatments.