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Multiple Disease Prediction Web App

A web application for predicting multiple diseases using machine learning models. This project includes prediction models for diabetes, Parkinson's disease, heart disease, and breast cancer.

Table of Contents

• About
• Installation
• Usage
• Models
• Contributing
• License

About

This web app provides a user-friendly interface to predict multiple diseases based on various input features. The machine learning models used in this application are trained on relevant datasets to make accurate predictions.

The diseases currently supported by this web app include:

• Diabetes
• Parkinson's disease
• Heart disease
• Breast cancer

Web App

• Access the Web App - Use the web app to predict multiple diseases.

Installation

1. Clone the repository:

git clone https://github.com/AryanKaushal2002/Multiple-Disease-Prediction-Model-Deployment-using-StreamLit.git

2. Install the required dependencies:

pip install -r requirements.txt

Usage

1. Run the web app:

streamlit run app.py

2. Open your web browser and go to http://localhost:8080 to access the web app.

3. Select the disease prediction page you want to use and provide the required input features.

4. Click on the Test Result button to generate the prediction result.

Models

The machine learning models used in this web app are trained on publicly available datasets specific to each disease. Here is a brief description of each model:

• Diabetes Model: This model predicts the likelihood of a person having diabetes based on input features such as glucose level, blood pressure, BMI, etc.

• Parkinson's Disease Model: This model predicts the presence of Parkinson's disease in a person based on features extracted from voice recordings.

• Heart Disease Model: This model predicts the presence of heart disease based on various clinical and demographic features of a person.

• Breast Cancer Model: This model predicts whether a breast mass is malignant or benign using features derived from breast cytology.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvement, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.

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