CardioGuard AI • Neural Diagnostic System 🫀

CardioGuard AI is a state-of-the-art heart disease prediction system powered by machine learning. It utilizes a Random Forest Classifier to analyze patient biometric data and provide a real-time risk assessment. The application features a futuristic, "Ultra Premium" user interface designed for clarity, engagement, and professional medical aesthetics.

✨ Key Features

• 🤖 Advanced ML Engine: Powered by a robust Random Forest Classifier trained on clinical heart disease data.
• 🎨 Futuristic UI/UX: Immersive interface with animated backgrounds, particle effects, and glassmorphism design.
• 📊 Interactive Biometrics: Easy-to-use sliders and dropdowns for inputting patient data (Age, BP, Cholesterol, etc.).
• ⚡ Real-time Analysis: Instant risk probability calculation with visual feedback.
• 📈 Dynamic Visualizations:
  • Animated gauge charts for risk scoring.
  • Key health metrics dashboard.
  • Lottie animations for visual engagement.
• 🔄 Auto-Healing: Automatically retrains the model if the model file is missing or incompatible.

🛠️ Tech Stack

• Frontend: Streamlit
• Machine Learning: Scikit-learn
• Data Processing: Pandas
• Visualization: Plotly, Streamlit-Lottie
• Model Serialization: Joblib

📂 Project Structure

├── app.py                  # Main Streamlit application
├── train_model.py          # Model training script
├── heart.csv               # Dataset for training
├── model.joblib            # Trained Random Forest model
├── healthy_profile.joblib  # Reference profile for default values
├── requirements.txt        # Python dependencies
└── README.md               # Project documentation

🚀 Installation & Setup

1. Clone the repository (or download the files):

   git clone <repository-url>
   cd heart-disease-prediction

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # macOS/Linux
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Train the Model (First Run): If model.joblib is missing, the app will attempt to train it automatically. You can also manually train it:

   python train_model.py

💻 Usage

1. Run the application:

   streamlit run app.py

2. Navigate the Interface:

   • Enter patient details in the Biometric Input section.
   • Adjust sliders for numerical values (Age, Blood Pressure, etc.).
   • Select options for categorical values (Chest Pain Type, ECG results, etc.).

3. Analyze:

   • Click the ⚡ INITIATE ANALYSIS button.
   • View the Risk Probability gauge and detailed recommendation card.

⚠️ Medical Disclaimer

CardioGuard AI is a demonstration tool for educational and research purposes only.

• It is NOT a substitute for professional medical advice, diagnosis, or treatment.
• Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition.
• Do not disregard professional medical advice or delay in seeking it because of something you have read on this application.

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© 2025 CardioGuard AI | Neural Diagnostic System