🩺 Multiple Disease Prediction System using Machine Learning & Streamlit

A web-based clinical decision support tool that predicts Diabetes, Heart Disease, and Parkinson's Disease using machine learning models. Built with Python, powered by Streamlit, and trained on real-world medical datasets.

---

🔍 Features

• ✅ Predicts three diseases from one unified interface.
• ✅ Clean and interactive UI powered by Streamlit.
• ✅ Models trained using algorithms like SVM, Logistic Regression, and more.
• ✅ Form-based input for patient records.
• ✅ Downloadable diagnostic reports.
• ✅ Standardized input scaling for better accuracy.
• ✅ Model persistence using Pickle.

---

🧠 Algorithms Used

• Support Vector Machine (SVM)
• Logistic Regression
• Random Forest
• K-Nearest Neighbors
• Gaussian Naive Bayes
• Decision Tree

---

📊 Datasets Used

Disease	Dataset Source
Diabetes	Kaggle - Diabetes Dataset
Heart Disease	Kaggle - Heart Disease Dataset
Parkinson’s	UCI ML Repository - Parkinson’s Data

---

⚙️ Tech Stack

• Python – Core language for model development and data handling.
• Scikit-learn – Model training and evaluation.
• Pandas & NumPy – Data processing and manipulation.
• Streamlit – Web app development.
• Pickle – Saving and loading models/scalers.

---

🚀 How to Run Locally

1. Clone this repo

   git clone https://github.com/your-username/multiple-disease-prediction.git
   cd multiple-disease-prediction
   

2. Install dependencies It's recommended to use a virtual environment.

   pip install -r requirements.txt

3.Run the app

  streamlit run app.py

📌 Future Scope

1. Integration with real-time medical IoT devices.
2. Add more diseases like cancer or kidney disease.
3. Deploy as a mobile app.
4. Multilingual and voice-enabled inputs.
5. Secure login for doctors and patients.

⭐ Give a Star!

If you found this helpful, feel free to ⭐ the repository and share it with others!

📄 License

This project is open source and available under the MIT License.