🩺 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. 🌟

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📚 Table of Contents

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

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📖 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.

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✨ 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.

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📂 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

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⚙️ 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

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🚀 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. 🖥️✨

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🛠️ 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.

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📊 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).

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🙏 Acknowledgements

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

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