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🩺 Smart Health - Disease Prediction using Data Mining 🧠💻

This project, Smart Health, uses data mining techniques to predict diseases based on patient symptoms. It employs the Random Forest Classifier as the primary machine learning model and provides a user-friendly interface powered by Streamlit. 🌟 Screenshot 2024-12-02 145325

📚 Table of Contents

  1. 📖 Overview
  2. ✨ Features
  3. 📂 Project Structure
  4. ⚙️ Setup Instructions
  5. 🚀 Usage
  6. 🛠️ Technologies Used
  7. 📊 Dataset
  8. 🙏 Acknowledgements

📖 Overview

Smart Health is a machine learning-based web application designed to assist in diagnosing diseases. It uses a dataset of patient symptoms to train a predictive model and provides insights into the likelihood of various diseases. 🧬

Key Highlights:

  • 🔍 High-accuracy predictions using Random Forest.
  • 🌐 A Streamlit-based interactive and intuitive web application.

✨ Features

  • ✅ Train and save machine learning models with ease.
  • 📈 Evaluate model performance on validation and test data.
  • 🔮 Predict diseases based on symptoms or test datasets.
  • 🖥️ User-friendly and interactive UI for non-technical users.

📂 Project Structure

smart_health/
│
├── dataset/
│   ├── training_data.csv    # 📄 Training dataset
│   ├── test_data.csv        # 📄 Test dataset
│
├── saved_model/
│   └── random_forest.joblib # 🛠️ Saved model
│
├── venv/                    # 🌐 Python virtual environment
│
├── app.py                   # 🖥️ Streamlit app file
├── config.yaml              # ⚙️ Configuration file
├── main.py                  # 🧠 Script for training the model
├── test_model.py            # 🔬 Script for testing the model
├── requirements.txt         # 📦 Python dependencies

⚙️ Setup Instructions

  1. Clone the repository:

    git clone <repository-url>
cd smart_health

  2. Create a virtual environment:

    python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

  3. Install dependencies:

    pip install -r requirements.txt

  4. Update the configuration: Edit the config.yaml file to specify paths for datasets, saved models, and other parameters. 📝

  5. Run the application:

    streamlit run app.py

🚀 Usage

🧠 Training the Model

  1. Specify the model type (e.g., Random Forest) in main.py.
  2. Run the script: 
    python main.py
    The trained model will be saved in the saved_model/ directory. 🛠️

🔬 Testing the Model

Evaluate the model's performance using test_model.py:

python test_model.py

🌐 Using the Streamlit App

Launch the app:

streamlit run app.py

Use the web interface to make disease predictions. 🖥️✨

🛠️ Technologies Used

  • Python 🐍: Core programming language.
  • Streamlit 🌐: For building the web interface.
  • Scikit-learn 📚: For machine learning models.
  • Pandas 🐼: For data manipulation.
  • PyYAML 📝: For configuration management.

📊 Dataset

The project uses two datasets:

  1. training_data.csv: 📄 Dataset for training the model.
  2. test_data.csv: 📄 Dataset for testing the model.

Each dataset includes:

  • 🩺 Patient symptoms (features).
  • 🏥 Disease prognosis (labels).

🙏 Acknowledgements

  • 📘 Scikit-learn Documentation for machine learning references.
  • 🌟 Streamlit Community for guidance on app development.