website: https://behealthy-uqhrtfdwxtdpwzhuappjoia.streamlit.app/

This web application, built with Streamlit, predicts the likelihood of three diseases: Diabetes, Heart Disease, and Parkinson's Disease. Each prediction is powered by a separate machine learning model trained specifically for that condition, and the models are stored as .sav files.

• Overview
• Features
• Requirements
• Setup and Installation
• Project Structure
• How to Use
• Model Information

The application provides a streamlined, user-friendly interface for disease risk prediction:

• Diabetes Prediction: Considers health metrics like pregnancies, glucose level, BMI, and more.
• Heart Disease Prediction: Analyzes factors such as age, cholesterol levels, and heart rate.
• Parkinson's Prediction: Uses voice and movement metrics, including jitter and shimmer values.

Each disease model is pre-trained, loaded from separate .sav files, and provides real-time predictions based on user input.

• Interactive Sidebar Navigation: Easily switch between the three disease prediction models.
• Custom Input Fields: Each disease model has specific fields for user data entry.
• Real-Time Prediction Output: Instant feedback on disease likelihood.

• Python 3.7+
• Streamlit
• streamlit-option-menu
• scikit-learn (for loading the pre-trained models)
• Pickle (used to load serialized models)

These dependencies are specified in the requirements.txt file. Installing this file will automatically set up your environment with all necessary packages.

1. Clone the repository:

   git clone https://github.com/your-username/multipleDiseasePrediction.git
   cd multipleDiseasePrediction

2. Install dependencies from requirements.txt:

   pip install -r requirements.txt

   The requirements.txt file includes:

   • streamlit: The main framework for building the web application.
   • streamlit-option-menu: To create the sidebar navigation.
   • scikit-learn: Required for handling and making predictions with the pre-trained models.
   • Additional dependencies for handling machine learning models.

3. Place the Trained Model Files:

   • The project expects separate saved models for each disease in the savedModels directory:
     • trainedModel_RF.sav for Diabetes prediction.
     • heartTrainedModel.sav for Heart Disease prediction.
     • parkinsonTrainedModel.sav for Parkinson's Disease prediction.
   • If using a different folder structure, update the file paths in the code.

4. Run the Streamlit app:

   streamlit run app.py

• app.py: Main Streamlit application script.
• savedModels/: Folder containing .sav files for each disease model.
  • trainedModel_RF.sav – Diabetes Prediction Model
  • heartTrainedModel.sav – Heart Disease Prediction Model
  • parkinsonTrainedModel.sav – Parkinson's Prediction Model
• requirements.txt: Contains required libraries for setting up the project.

1. Run the app and open it in your browser (usually at http://localhost:8501).
2. Use the sidebar to select a disease prediction model.
3. Input relevant health data for the chosen condition in the provided fields.
4. Click the Predict button to get an immediate prediction.

The models are trained using public datasets and saved as .sav files for quick loading:

• Diabetes Prediction Model: Trained on the PIMA Indian Diabetes Dataset.
• Heart Disease Prediction Model: Uses data from the UCI Heart Disease Dataset.
• Parkinson's Prediction Model: Based on voice metrics from the UCI Parkinson’s dataset.

These models use a machine learning algorithm (Random Forest) and were developed for educational purposes.