{"id":15063938,"url":"https://github.com/akashshnkr/multi-disease-prediction","last_synced_at":"2026-01-02T20:39:13.171Z","repository":{"id":257310837,"uuid":"852338517","full_name":"AkashShnkr/Multi-Disease-Prediction","owner":"AkashShnkr","description":"Developed and integrated three machine learning models for predicting diabetes, Parkinson's, and heart disease into a Streamlit-based web application. 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The application enhances early detection and improves healthcare outcomes by providing users with accurate health predictions based on their input data..\n\n## Tech Stack\n\n* ![Python][Python]\n* ![Numpy][Numpy]\n* ![Pandas][Pandas]\n* ![Scikit-learn][Scikit-learn]\n* ![Streamlit][Streamlit]\n\n[Python]: https://img.shields.io/badge/Python-3776AB?style=for-the-badge\u0026logo=python\u0026logoColor=white\n[Numpy]: https://img.shields.io/badge/Numpy-013243?style=for-the-badge\u0026logo=numpy\u0026logoColor=white\n[Pandas]: https://img.shields.io/badge/Pandas-150458?style=for-the-badge\u0026logo=pandas\u0026logoColor=white\n[Scikit-learn]: https://img.shields.io/badge/Scikit_learn-F7931E?style=for-the-badge\u0026logo=scikit-learn\u0026logoColor=white\n[Streamlit]: https://img.shields.io/badge/Streamlit-FF4B4B?style=for-the-badge\u0026logo=streamlit\u0026logoColor=white\n\n\n\n## 🎯 Features\n\n- **Disease Prediction**:  Predict the presence of diabetes, Parkinson's disease, and heart disease\n- **Real-Time Input**: Input data via camera for convenience\n- **Automated Invoicing**: Streamlined process for generating and   managing invoices.\n- **User-Friendly Interface**:  Built with Streamlit for a seamless user experience\n\n\n## Demo\n\nInsert gif or link to demo\n\n\n\n\n\n![Logo](https://drive.google.com/file/d/1W2ldGxUD_HR2exV-EUb32CFAxp8A5N04/view?usp=sharing)\n\n\n\n\n\n\n\n\n\n## 📂 Project Structure\n\nThe project is divided into two main parts:\n\n```bash\n/project-root\n│\n├── /bin\n│   └── express-mcsr-init\n│\n├── /lib\n│   └── generateStructure.js\n│\n├── package-lock.json\n├── package.json\n└── README.md\n\n```\n# 🚀 Getting Started\n\nFollow the instructions below to set up the project locally.\n\n## 🔧 Installation\n\n1. **Clone the repository:**\n\n   ```bash\n   git clone https://github.com/yourusername/disease-prediction-app.git\ncd disease-prediction-app\n\n   ```\n2. **Set Up a Virtual Environment (Optional but recommended):**\n\n\n```bash\npython -m venv venv\nsource venv/bin/activate  # On Windows, use `venv\\Scripts\\activate`\n\n```\n3. **Install dependencies:**\n\n\n```bash\npip install -r requirements.txt\n\n```\n\n## 🛠️ Running the Project\n4. **Start the backend server:**\n\n```bash\nstreamlit run app.py\n```\nThe application will open in your default web browser.\n\n\n## How It Works\n- **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.\n- **Data Input**:Users can provide input data through a camera interface integrated into the application.\n- **Prediction**: The Streamlit framework processes the input data and displays the prediction results in real time.\n\n## 📦 Deployment\nContributions are welcome! Please feel free to submit a Pull Request or open an issue.\n\n## 🤝 Contributing\nContributions are welcome! Please feel free to submit a pull request or open an issue.\n\n## 📜 License\nThis project is licensed under the MIT License.\n\n## 🌟 Acknowledgements\nMade with ❤️by Akash Shankar(https://akashdevweb.netlify.app/).\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakashshnkr%2Fmulti-disease-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakashshnkr%2Fmulti-disease-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakashshnkr%2Fmulti-disease-prediction/lists"}