CardioSafe-AI-Heart-Disease-Risk-Predictor-App

# ❤️ CardioSafe AI - Heart Disease Risk Prediction System

![Streamlit App](https://cardiosafe-ai-heart-disease-risk-predictor-app-ankit-parwatkar.streamlit.app/)
![GitHub last commit](https://github.com/ankitparwatkar/CardioSafe-AI-Heart-Disease-Risk-Predictor-App)

An intelligent web application that predicts cardiovascular disease risk using machine learning. Designed for both patients and healthcare professionals with an interactive clinical interface.

## 🌟 Features

- **Patient Risk Assessment**  
  Input 11 clinical parameters including age, cholesterol levels, and ECG metrics
- **Real-Time Prediction**  
  Instant risk classification with probability percentage
- **Clinical Guidance**  
  Actionable recommendations based on risk level
- **Interactive Visualization**  
  Dynamic progress bars and particle effects
- **Responsive Design**  
  Mobile-friendly interface with clinical-grade UI

## 🚀 Try the Live Demo

[![Open in Streamlit](https://cardiosafe-ai-heart-disease-risk-predictor-app-ankit-parwatkar.streamlit.app/)]

## 📥 Installation

1. Clone repository:
   ```bash
   git clone https://github.com/ankitparwatkar/CardioSafe-AI-Heart-Disease-Risk-Predictor-App.git
   cd CardioSafe-AI

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app:

   streamlit run app.py

📊 Dataset Overview

Parameter	Description	Range
Age	Patient's age in years	20-100
Resting BP	Blood pressure (mm Hg)	90-200
Cholesterol	Serum level (mg/dl)	100-600
ST Depression	Exercise-induced measurement	0-6.2
Max Heart Rate	Achieved during test	70-220

Target Variable: Risk Percentage %

🧠 Machine Learning Model

• Algorithm: Random Forest Classifier (Pre-trained)
• Accuracy: 92.4% on test set
• Features: 11 clinical parameters
• Output: Risk probability with interpretable results

📸 Application Highlights

🛠️ Technical Stack

• Frontend: Streamlit + Custom CSS
• Backend: Python 3.9+
• ML Framework: Scikit-learn + XGBoost
• Visualization: Particles.js + Animated CSS

👨💻 Developer Profile

Ankit Parwatkar
📍 Machine Learning Engineer | Healthcare AI Specialist

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⚠️ Clinical Disclaimer: This tool provides risk estimations and should not replace professional medical advice. Always consult a qualified healthcare provider for diagnosis and treatment.