Heart Disease Prediction System 🫀

A comprehensive machine learning system for predicting heart disease risk using patient data. This project provides multiple interfaces (web, console, and executable) with advanced visualization and explanation capabilities.

[pip install numpy==1.24.3 pandas==2.0.3 scikit-learn==1.3.0 matplotlib==3.7.2 seaborn==0.12.2 joblib==1.3.1 gradio==4.19.2 pyinstaller==6.5.0]

🌟 Features

🔬 Core Prediction Engine

• Random Forest Classifier with hyperparameter optimization
• Feature Importance Analysis to identify key risk factors
• Personalized Risk Assessment with detailed explanations
• Real-time Probability Scoring (0-100% risk scale)
• Medical Context Integration with BMI calculations

🎨 Advanced Visualizations

• Interactive Risk Gauge with color-coded severity levels
• Feature Impact Charts showing positive/negative contributions
• Category-based Risk Analysis (Demographics, Symptoms, Vital Signs, etc.)
• Personalized Recommendations based on individual risk factors
• Professional Medical-style Reports with clean white backgrounds

🖥️ Multiple User Interfaces

1. Web Interface (Gradio)

• Modern, responsive web UI accessible via browser
• Real-time predictions with interactive controls
• Enhanced visualization dashboard
• Mobile-friendly design
• Automatic BMI calculation

2. Console Interface

• Command-line interface for quick predictions
• Step-by-step input guidance
• Detailed result explanations
• Perfect for automation and scripting

3. Standalone Executable

• PyInstaller-built Windows executable
• No Python installation required
• Portable distribution
• Complete self-contained application

📊 Comprehensive Feature Set

Demographic Features

• Age (20-100 years)
• Gender (Male/Female)
• Weight and Height (with automatic BMI calculation)

Medical History

• Chest Pain Type (4 categories: Typical angina, Atypical angina, Non-anginal pain, Asymptomatic)
• Resting Blood Pressure (80-200 mmHg)
• Serum Cholesterol (100-600 mg/dl)
• Fasting Blood Sugar (>120 mg/dl indicator)
• Family History of Heart Disease

ECG & Heart Tests

• Resting Electrocardiographic Results (Normal, ST-T abnormality, Left ventricular hypertrophy)
• Maximum Heart Rate Achieved (60-220 bpm)
• Exercise-Induced Angina (Yes/No)

Exercise Test Results

• ST Depression (0-10 range)
• Peak Exercise ST Segment Slope (Upsloping, Flat, Downsloping)

Advanced Diagnostics

• Number of Major Vessels (0-3) colored by fluoroscopy
• Thalassemia Test Results (Normal, Fixed defect, Reversible defect)

🧠 Machine Learning Capabilities

Model Training & Evaluation

• Automated data preprocessing with feature scaling
• Cross-validation with GridSearchCV
• Comprehensive evaluation metrics:
  • Accuracy, Precision, Recall, F1-Score
  • ROC-AUC Score
  • Confusion Matrix
  • Classification Report

Feature Engineering

• Automatic BMI calculation from height/weight
• Feature importance ranking
• Categorical encoding (One-hot encoding)
• Numerical feature standardization

Model Persistence

• Joblib-based model serialization
• Automatic model saving/loading
• Preprocessor state preservation
• Cross-session compatibility

📈 Analysis & Reporting Tools

Feature Importance Analysis

• Visual feature importance plots
• Top contributing factors identification
• Category-wise risk breakdown
• Medical context explanations

Personalized Recommendations

• Risk-specific lifestyle suggestions
• Medical consultation recommendations
• Exercise and diet guidance
• Monitoring suggestions based on risk factors

Risk Level Classification

• Low Risk (<20%): Minimal intervention needed
• Moderate Risk (20-50%): Lifestyle modifications recommended
• High Risk (50-80%): Medical consultation advised
• Very High Risk (>80%): Immediate medical attention suggested

🚀 Quick Start

Prerequisites

Python 3.8+
pip (Python package manager)

Installation

1. Clone the repository

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

2. Install dependencies

   pip install -r requirements.txt

3. Run the application

Option A: Web Interface (Recommended)

   python heart_disease_app.py

Access the web interface at http://localhost:7860

Option B: Command Line Interface

python main.py

Option C: Feature Analysis with Gradio

python analyze_features.py

Option D: Feature-Only Analysis

python analyze_features_only.py  # Generate feature importance plots without UI

Option E: Simple App Version

python simple_heart_app.py  # Minimal version for basic predictions

📦 Building Standalone Executable

Create a portable Windows executable:

   python build_exe.py

Or use the batch file:

build_exe.bat

The executable will be created in the dist/ folder.

Quick Launch Options

For Windows users, you can also use:

run_heart_disease_app.bat  # Quick launcher for web interface

📋 Usage Examples

Web Interface Usage

1. Launch the application: python heart_disease_app.py
2. Open browser to the provided URL
3. Fill in patient information using the intuitive sliders and dropdowns
4. Click "Predict Heart Disease Risk"
5. View comprehensive results with visualizations and recommendations

Console Interface Usage

from src.ui.console_interface import ConsoleInterface

interface = ConsoleInterface('models/heart_disease_model.joblib')
interface.run()

Programmatic Usage

from src.models.model import HeartDiseaseModel

# Load trained model
model = HeartDiseaseModel.load('models/heart_disease_model.joblib')

# Make prediction
input_data = {
    'age': 45, 'sex': 1, 'cp': 0, 'trestbps': 130,
    'chol': 250, 'fbs': 0, 'restecg': 0, 'thalach': 150,
    'exang': 0, 'oldpeak': 1.0, 'slope': 0, 'ca': 0, 'thal': 1,
    'height': 175, 'weight': 80
}

explanation = model.explain_prediction(input_data)
print(f"Risk Level: {explanation['risk_level']}")
print(f"Probability: {explanation['probability']:.1%}")

🏗️ Project Structure

Heart Disease Prediction T/
├── 📁 src/                          # Source code modules
│   ├── 📁 data/                     # Data-related modules
│   │   ├── feature_definitions.py   # Feature specifications & descriptions
│   │   └── __init__.py
│   ├── 📁 models/                   # Machine learning models
│   │   ├── heart_disease_model.py   # Main ML model implementation
│   │   ├── model.py                 # Alternative model interface
│   │   ├── feature_importance.py    # Feature analysis tools
│   │   └── __init__.py
│   ├── 📁 preprocessing/            # Data preprocessing
│   │   ├── preprocessor.py          # Data cleaning & transformation
│   │   └── __init__.py
│   ├── 📁 ui/                       # User interfaces
│   │   ├── gradio_interface.py      # Web interface implementation
│   │   ├── console_interface.py     # Command-line interface
│   │   └── __init__.py
│   ├── 📁 utils/                    # Utility functions
│   │   ├── data_loader.py           # Data loading & sample generation
│   │   └── __init__.py
│   └── __init__.py
├── 📁 models/                       # Trained model storage
│   └── heart_disease_model.joblib   # Saved model file
├── 📄 heart_disease_app.py          # Main web application
├── 📄 main.py                       # CLI application entry point
├── 📄 analyze_features.py           # Feature analysis script
├── 📄 build_exe.py                  # Executable builder
├── 📄 example.py                    # Usage examples
├── 📄 analyze_features_only.py      # Feature-only analysis script
├── 📄 simple_heart_app.py           # Simplified application version
├── 📄 requirements.txt              # Python dependencies
├── 📄 run_heart_disease_app.bat     # Windows launcher
├── 📄 build_exe.bat                 # Windows build script
├── 📄 feature_importance.png        # Generated feature importance plot
├── 📄 *.spec                        # PyInstaller specification files
└── 📄 README.md                     # This file

🔧 Dependencies

Core Libraries

• numpy==1.24.3 - Numerical computing
• pandas==2.0.3 - Data manipulation
• scikit-learn==1.3.0 - Machine learning algorithms
• joblib==1.3.1 - Model serialization

Visualization

• matplotlib==3.7.2 - Plotting and visualization
• seaborn==0.12.2 - Statistical visualizations

User Interface

• gradio==4.19.2 - Web interface framework

Deployment

• pyinstaller==6.5.0 - Executable creation

🎯 Key Algorithms & Techniques

Machine Learning

• Random Forest Classifier - Ensemble learning for robust predictions
• GridSearchCV - Hyperparameter optimization
• Cross-Validation - Model validation and selection
• Feature Importance - Understanding model decisions

Data Processing

• One-Hot Encoding - Categorical variable handling
• Standard Scaling - Numerical feature normalization
• Missing Value Handling - Data quality assurance
• Feature Engineering - BMI calculation and derived features

Visualization Techniques

• Risk Gauges - Intuitive probability display
• Feature Impact Charts - Contribution analysis
• Category Grouping - Medical domain organization
• Color Coding - Risk level visualization

🎨 Visualization Features

Enhanced Risk Dashboard

• Professional Medical Theme with clean white backgrounds
• Interactive Risk Gauge with color-coded severity (Green → Red)
• Feature Impact Visualization showing positive/negative contributions
• Category-based Analysis grouping features by medical domain
• Personalized Recommendations with medical icons and actionable advice
• Real-time BMI Calculator integrated into the interface
• Medical Context Explanations for each feature and risk factor

Feature Categories

• 🎂 Demographics - Age, Gender, BMI
• 💓 Symptoms - Chest pain patterns
• 🩺 Vital Signs - Blood pressure, heart rate
• 🧪 Blood Tests - Cholesterol, blood sugar
• 📈 Heart Tests - ECG results
• 🏃 Exercise Tests - Stress test results
• 🔍 Imaging - Vessel blockages, perfusion
• 🏃‍♂️ Physical - Height, weight, derived metrics

Recent Improvements

• ✅ Fixed matplotlib title font conflicts for better compatibility
• ✅ Clean white backgrounds throughout all visualizations
• ✅ Enhanced medical styling with professional appearance
• ✅ Improved error handling and user feedback

📊 Model Performance

The Random Forest model achieves:

• High Accuracy on validation datasets
• Balanced Precision/Recall for both classes
• Robust Feature Importance rankings
• Reliable Probability Estimates for risk assessment

Note: Actual performance metrics depend on the training dataset used.

🛡️ Medical Disclaimer

⚠️ IMPORTANT: This application is designed for educational and research purposes only. It should NOT be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare providers for medical decisions.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Medical feature definitions based on established cardiology research
• UCI Heart Disease Dataset for reference
• Scikit-learn community for machine learning tools
• Gradio team for the excellent web interface framework

🔧 Troubleshooting

Common Issues

Matplotlib Font Errors

• Fixed: Title font conflicts resolved in latest version
• All backgrounds now use clean white styling

Model Not Found

• Run python main.py to train a new model automatically
• Model will be saved to models/heart_disease_model.joblib

Import Errors

• Ensure all dependencies are installed: pip install -r requirements.txt
• Check Python version compatibility (3.8+)

Executable Build Issues

• Use python build_exe.py instead of direct PyInstaller commands
• Ensure all dependencies are properly installed

Performance Tips

• Use the web interface for best user experience
• Console interface is faster for batch predictions
• Feature analysis scripts help understand model behavior

📞 Support

For questions, issues, or contributions:

• Open an issue on GitHub
• Check the documentation in the src/ modules
• Review the example usage in example.py
• Test with different interfaces to find what works best for your use case

Available Interfaces Summary

1. heart_disease_app.py - Full-featured web interface (Recommended)
2. main.py - Command-line interface with training options
3. analyze_features.py - Feature analysis with web UI
4. analyze_features_only.py - Feature analysis without UI
5. simple_heart_app.py - Minimal prediction interface

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Made with ❤️ for better health outcomes ⭐ Star this repository if you find it useful! ⭐

❤️ Heart Disease Prediction System

🌟 Developed by Samyak Kamble

A comprehensive machine learning system for predicting heart disease risk with an interactive and user-friendly interface.

✨ Key Features

• 🩺 Advanced Risk Prediction: Uses machine learning to predict heart disease risk with high accuracy
• 📊 Interactive Visualizations: Dynamic feature impact graphs showing how each factor affects risk
• 🎯 Personalized Recommendations: Custom health suggestions based on individual risk factors
• 📱 User-Friendly Interface: Large text and intuitive design for easy use by all age groups
• 🔍 Real-time BMI Calculation: Instantly calculates and categorizes BMI from height and weight
• 🌐 Shareable Public Link: Access the tool from anywhere via a public URL
• 💻 Standalone Application: Can be packaged as an executable (.exe) for offline use
• 📈 Educational Information: Provides medical context for each risk factor

📋 Medical Factors Analyzed

Factor	Description	Impact
👴 Age	Risk doubles every decade after 45	High
♂️ Gender	Men have 2-3x higher risk before age 55	Medium
💔 Chest Pain	Typical angina strongly indicates coronary artery disease	Very High
🩸 Blood Pressure	Each 20mmHg increase doubles risk	High
🍔 Cholesterol	23% increased risk per 40mg/dl above 200	High
🧪 Blood Sugar	Diabetes doubles heart disease risk	Medium
📈 ECG Results	ST-T abnormalities indicate 5x higher risk	High
❤️ Max Heart Rate	Lower max heart rate indicates decreased function	Medium
😣 Exercise Angina	Presence indicates 3x higher risk	High
📉 ST Depression	Values >2mm indicate severe ischemia	High
📊 ST Slope	Downsloping indicates poor prognosis	Medium
🩺 Major Vessels	Risk increases 2x per vessel affected	High
🔬 Thalassemia	Reversible defects indicate 3x higher risk	High
⚖️ BMI	BMI >30 increases risk by 50%	Medium

🧠 Machine Learning Model

• Algorithm: Random Forest Classifier
• Features: 13 clinical parameters + BMI
• Metrics:
  • Accuracy: ~85%
  • Precision: ~84%
  • Recall: ~86%
  • F1 Score: ~85%
  • ROC AUC: ~90%

🖥️ Technical Implementation

📚 Project Structure

Heart Disease Prediction/
  ├── models/                  # Trained model files
  ├── src/                     # Source code
  │   ├── data/                # Data definitions and processing
  │   ├── models/              # ML model implementation
  │   ├── preprocessing/       # Data preprocessing
  │   ├── ui/                  # User interfaces
  │   └── utils/               # Utility functions
  ├── heart_disease_app.py     # Main application
  ├── build_exe.py             # Executable builder
  ├── main.py                  # CLI version
  └── requirements.txt         # Dependencies

🛠️ Technologies Used

• Python 3.8+: Core programming language
• Scikit-learn: Machine learning algorithms
• Pandas/NumPy: Data processing
• Matplotlib/Seaborn: Data visualization
• Gradio: Web interface
• PyInstaller: Executable packaging

📦 Installation

Requirements

numpy>=1.19.5
pandas>=1.3.0
scikit-learn>=0.24.2
matplotlib>=3.4.2
seaborn>=0.11.1
gradio>=3.0.0
joblib>=1.0.1
pyinstaller>=5.0.0  # For executable creation

🚀 Quick Start

1. Clone the repository

   git clone https://github.com/samyak2403/Heart-Disease-Prediction-T.git
   cd heart-disease-prediction
   

2. Install dependencies

   pip install -r requirements.txt
   

3. Run the web application

   python heart_disease_app.py
   

4. Build executable (optional)

   python build_exe.py
   

💻 Usage

Web Interface

1. Enter patient information in the form
2. Click "SUBMIT" to generate prediction
3. View results including:
   • Heart disease risk prediction
   • Risk level assessment
   • Contributing factors
   • Interactive feature impact visualization
   • Personalized health recommendations

Executable Version

1. Run the generated .exe file
2. Follow the same steps as the web interface

📊 Feature Impact Visualization

The system provides a detailed visualization of how each factor contributes to heart disease risk:

• 📈 Color-coded bars: Red for risk-increasing factors, green for risk-decreasing factors
• 🔢 Numerical impact: Precise quantification of each factor's contribution
• ℹ️ Educational tooltips: Medical information about each risk factor
• 🎯 Risk gauge: Visual representation of overall heart disease risk
• 💡 Personalized recommendations: Tailored health advice based on risk factors

🔄 Model Training

The model is trained on a comprehensive dataset of heart disease cases with the following steps:

1. Data preprocessing and normalization
2. Feature engineering and selection
3. Model training with cross-validation
4. Hyperparameter optimization
5. Performance evaluation on test data

👨‍💻 Developer Information

This project was developed by Samyak Kamble as a comprehensive heart disease risk assessment tool combining medical knowledge with advanced machine learning techniques.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📞 Contact

For questions, suggestions, or collaborations, please contact:

• Samyak Kamble
• Email: samyak.kamble@example.com
• LinkedIn: linkedin.com/in/samyak-kamble

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