❤️ CardioGuard AI

Intelligent Cardiovascular Disease Prediction System

🚀 Live App: https://cardioguard-innfk687tnac4t5fzekefz.streamlit.app/

💻 GitHub Repository: https://github.com/omkumar103/CardioGuard

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🧠 Project Overview

CardioGuard AI is a Machine Learning-powered web application designed to predict the risk of cardiovascular disease using key health parameters.

It combines data science, machine learning, and an interactive Streamlit UI to deliver real-time, interpretable health insights in a clean and user-friendly interface.

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🎯 Key Features

✨ Real-Time Prediction

• Instant heart disease risk prediction based on user input

📊 Interactive Data Visualization

• Graphs for age, blood pressure, cholesterol, and more
• Correlation heatmap for feature relationships

🤖 Multiple ML Models

• Logistic Regression
• Random Forest
• Linear SVM

🏆 Model Comparison System

• Automatically selects the best-performing model

🧩 Feature Engineering

• BMI calculation
• Pulse pressure analysis
• Data cleaning & preprocessing

🎨 Modern UI/UX (Streamlit)

• Multi-page navigation (Home, About, Features, Prediction, Contact)
• Clean, responsive, and visually engaging interface

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📊 Dataset

• Cardiovascular Disease Dataset

• Includes features like:

  • Age
  • Gender
  • Height & Weight
  • Blood Pressure (ap_hi, ap_lo)
  • Cholesterol & Glucose levels
  • Lifestyle factors (smoking, alcohol, activity)

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⚙️ Tech Stack

• Language: Python

• Frontend/UI: Streamlit

• Libraries:

  • pandas, numpy
  • scikit-learn
  • matplotlib, seaborn

• Model Handling: joblib

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🧪 Machine Learning Workflow

1. Data Cleaning & Preprocessing
2. Feature Engineering
3. Train-Test Split
4. Model Training
5. Accuracy Evaluation
6. Best Model Selection
7. Real-Time Prediction Integration

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📈 Model Performance

Model	Accuracy
Logistic Regression	~72%
Random Forest	~73%
Linear SVM	~72%

👉 Best Model: Random Forest

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🚀 How to Run Locally

# Clone repository
git clone https://github.com/omkumar103/CardioGuard.git

# Navigate to project
cd CardioGuard

# Install dependencies
pip install -r requirements.txt

# Run Streamlit app
streamlit run app.py

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🌐 Deployment

The project is deployed using Streamlit Cloud:

🔗 https://cardioguard-innfk687tnac4t5fzekefz.streamlit.app/

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📌 Future Improvements

• 🔍 Advanced hyperparameter tuning
• 📱 Mobile-first UI enhancements
• 🧠 Deep learning model integration
• 📊 Personalized health recommendations
• 🔐 User authentication system

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👨‍💻 Author

Om Kumar 📧 Email: om084547@gmail.com 🔗 LinkedIn: https://www.linkedin.com/in/om-kumar-96bb11294/ 💻 GitHub: https://github.com/omkumar103

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⭐ Final Note

This project demonstrates the integration of Machine Learning + Real-world Healthcare Data + Interactive Web UI, making it a strong example of an end-to-end Data Science application.

If you found this project useful, consider ⭐ starring the repository!