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https://github.com/akashshnkr/multi-disease-prediction
Developed and integrated three machine learning models for predicting diabetes, Parkinson's, and heart disease into a Streamlit-based web application. The interface allows users to input data and receive accurate health predictions, enhancing early detection and healthcare outcomes.
https://github.com/akashshnkr/multi-disease-prediction
logistic-regression machine-learning-algorithms numpy pandas python scikit-learn streamlit-webapp svm
Last synced: 24 days ago
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Developed and integrated three machine learning models for predicting diabetes, Parkinson's, and heart disease into a Streamlit-based web application. The interface allows users to input data and receive accurate health predictions, enhancing early detection and healthcare outcomes.
- Host: GitHub
- URL: https://github.com/akashshnkr/multi-disease-prediction
- Owner: AkashShnkr
- Created: 2024-09-04T16:27:19.000Z (2 months ago)
- Default Branch: master
- Last Pushed: 2024-09-15T21:27:42.000Z (about 2 months ago)
- Last Synced: 2024-10-13T03:22:19.986Z (24 days ago)
- Topics: logistic-regression, machine-learning-algorithms, numpy, pandas, python, scikit-learn, streamlit-webapp, svm
- Language: Jupyter Notebook
- Homepage:
- Size: 68.4 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ⚕️Multiple Disease Prediction Web Application
**Multiple Disease Prediction Web Application** :
A Streamlit-based web application designed to predict the likelihood of diabetes, Parkinson's disease, and heart disease using machine learning models. The application enhances early detection and improves healthcare outcomes by providing users with accurate health predictions based on their input data..
## Tech Stack
* ![Python][Python]
* ![Numpy][Numpy]
* ![Pandas][Pandas]
* ![Scikit-learn][Scikit-learn]
* ![Streamlit][Streamlit]
[Python]: https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white
[Numpy]: https://img.shields.io/badge/Numpy-013243?style=for-the-badge&logo=numpy&logoColor=white
[Pandas]: https://img.shields.io/badge/Pandas-150458?style=for-the-badge&logo=pandas&logoColor=white
[Scikit-learn]: https://img.shields.io/badge/Scikit_learn-F7931E?style=for-the-badge&logo=scikit-learn&logoColor=white
[Streamlit]: https://img.shields.io/badge/Streamlit-FF4B4B?style=for-the-badge&logo=streamlit&logoColor=white
## 🎯 Features
- **Disease Prediction**: Predict the presence of diabetes, Parkinson's disease, and heart disease
- **Real-Time Input**: Input data via camera for convenience
- **Automated Invoicing**: Streamlined process for generating and managing invoices.
- **User-Friendly Interface**: Built with Streamlit for a seamless user experience
## Demo
Insert gif or link to demo
## 📂 Project Structure
The project is divided into two main parts:
```bash
/project-root
│
├── /bin
│ └── express-mcsr-init
│
├── /lib
│ └── generateStructure.js
│
├── package-lock.json
├── package.json
└── README.md
```
# 🚀 Getting Started
Follow the instructions below to set up the project locally.
## 🔧 Installation
1. **Clone the repository:**
```bash
git clone https://github.com/yourusername/disease-prediction-app.git
cd disease-prediction-app
```
2. **Set Up a Virtual Environment (Optional but recommended):**
```bash
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
3. **Install dependencies:**
```bash
pip install -r requirements.txt
```
## 🛠️ Running the Project
4. **Start the backend server:**
```bash
streamlit run app.py
```
The application will open in your default web browser.
## How It Works
- **Model Training**:Models are trained using datasets relevant to diabetes, Parkinson's, and heart disease. The algorithms used include Support Vector Machines (SVM) and logistic regression.
- **Data Input**:Users can provide input data through a camera interface integrated into the application.
- **Prediction**: The Streamlit framework processes the input data and displays the prediction results in real time.
## 📦 Deployment
Contributions are welcome! Please feel free to submit a Pull Request or open an issue.
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue.
## 📜 License
This project is licensed under the MIT License.
## 🌟 Acknowledgements
Made with ❤️by Akash Shankar(https://akashdevweb.netlify.app/).