🏥 Diabetes Prognosis and Risk Assessment System

📋 Table of Contents

• Overview
• Why This Project Matters
• Dataset Information
• Technologies Used
• Machine Learning Models
• Model Performance
• Web Application Features
• Installation & Setup
• Usage
• Project Structure
• Model Outputs & Visualizations
• Deployment
• Future Enhancements
• Contributing

🎯 Overview

The Diabetes Prognosis and Risk Assessment System is a comprehensive machine learning application that predicts diabetes risk levels based on 21 health indicators. This project demonstrates a complete ML pipeline from data analysis and model development to production deployment via a user-friendly web interface.

The system classifies patients into three risk categories:

• No Risk (Class 0): Patient shows no significant diabetes risk factors
• Mild Risk (Class 1): Patient has moderate diabetes risk factors requiring monitoring
• Severe Risk (Class 2): Patient has high diabetes risk factors requiring immediate attention

🌟 Why This Project Matters

Healthcare Impact

• Early Detection: Identifies diabetes risk before clinical symptoms appear
• Preventive Care: Enables proactive healthcare interventions
• Cost Reduction: Reduces healthcare costs through early prevention
• Population Health: Supports public health screening programs

Technical Significance

• Complete ML Pipeline: Demonstrates end-to-end machine learning workflow
• Model Comparison: Evaluates multiple algorithms for optimal performance
• Production Ready: Deployed web application with professional UI/UX
• Scalable Architecture: Modular design for easy maintenance and updates

📊 Dataset Information

Data Source

• File: diabetes_012_health_indicators.xls
• Size: 22.9 MB
• Format: Excel spreadsheet with comprehensive health indicators

Features (21 Health Indicators)

🩺 Medical Conditions

• HighBP: High Blood Pressure (0 = No, 1 = Yes)
• HighChol: High Cholesterol (0 = No, 1 = Yes)
• Stroke: History of Stroke (0 = No, 1 = Yes)
• HeartDiseaseorAttack: Heart Disease or Attack History (0 = No, 1 = Yes)

🚭 Lifestyle Factors

• Smoker: Smoking Status (0 = No, 1 = Yes)
• PhysActivity: Physical Activity (0 = No, 1 = Yes)
• Fruits: Regular Fruit Consumption (0 = No, 1 = Yes)
• Veggies: Regular Vegetable Consumption (0 = No, 1 = Yes)
• HvyAlcoholConsump: Heavy Alcohol Consumption (0 = No, 1 = Yes)

📈 Health Metrics

• BMI: Body Mass Index (Continuous variable)
• MentHlth: Mental Health Issues (0-30 days)
• PhysHlth: Physical Health Issues (0-30 days)
• GenHlth: General Health (1-5 scale: Excellent to Poor)

🏥 Healthcare Access

• CholCheck: Cholesterol Check (0 = No, 1 = Yes)
• AnyHealthcare: Any Healthcare Coverage (0 = No, 1 = Yes)
• NoDocbcCost: No Doctor due to Cost (0 = No, 1 = Yes)

🚶 Physical Limitations

• DiffWalk: Difficulty Walking (0 = No, 1 = Yes)

👥 Demographics

• Sex: Gender (0 = Female, 1 = Male)
• Age: Age in years
• Education: Education Level (1-6 scale)
• Income: Income Level (1-8 scale)

🛠️ Technologies Used

Core Technologies

# Machine Learning & Data Science
scikit-learn==1.2.2      # ML algorithms and preprocessing
pandas==1.5.3            # Data manipulation and analysis
numpy==1.24.3            # Numerical computing
joblib==1.2.0            # Model serialization

# Visualization
matplotlib==3.7.1        # Static plotting
seaborn==0.12.2          # Statistical data visualization

# Web Application
streamlit==1.37.0        # Web app framework

Development Environment

• Python: 3.12
• IDE: PyCharm/IntelliJ IDEA
• Version Control: Git
• Deployment: Heroku (Production Ready)

🤖 Machine Learning Models

1. 🎯 Logistic Regression (Production Model)

Status: ✅ Currently Deployed

# Model Configuration
LogisticRegression(
    max_iter=2000,
    solver='lbfgs',
    random_state=42
)

Key Features:

• Linear decision boundaries for multi-class classification
• Standard scaling preprocessing
• Coefficient-based feature importance
• ROC curve analysis
• Confusion matrix evaluation

Why Chosen for Production:

• High interpretability for healthcare decisions
• Stable performance across different datasets
• Fast inference time for real-time predictions
• Well-understood by medical professionals

2. 🌳 Random Forest Classifier

Status: 🧪 Experimental Model

# Model Configuration
RandomForestClassifier(
    n_estimators=100,
    random_state=42
)

Key Features:

• Ensemble learning with 100 decision trees
• Built-in feature importance ranking
• Robust to overfitting
• Handles non-linear relationships

3. 🚀 Gradient Boosting Classifier

Status: 🧪 Advanced Experimental Model

# Model Configuration
GradientBoostingClassifier(
    n_estimators=100,
    learning_rate=0.1,
    random_state=42,
    validation_fraction=0.1,
    n_iter_no_change=10
)

Key Features:

• Sequential learning for improved accuracy
• Hyperparameter tuning with RandomizedSearchCV
• Early stopping to prevent overfitting
• Advanced uncertainty detection
• Enhanced prediction confidence analysis

📈 Model Performance

🏆 Performance Comparison

Model	Accuracy	Status	Key Strengths
Logistic Regression	84.83%	🟢 Production	Interpretable, Fast, Reliable
Random Forest	84.11%	🟡 Research	Robust, Feature Importance
Gradient Boosting	~85%	🟡 Research	High Accuracy, Advanced Features

📊 Detailed Performance Metrics

Logistic Regression (Production Model)

Accuracy: 84.83%
Overall Performance: 0.85

Classification Report:
              precision    recall  f1-score   support
           0       0.85      0.95      0.90     35346
           1       0.42      0.21      0.28      4631
           2       0.74      0.64      0.69     10759
    accuracy                           0.85     50736
   macro avg       0.67      0.60      0.62     50736
weighted avg       0.81      0.85      0.82     50736

Model Interpretation

• Class 0 (No Risk): Excellent precision (0.85) and recall (0.95)
• Class 1 (Mild Risk): Challenging to predict due to subtle symptoms
• Class 2 (Severe Risk): Good balance of precision (0.74) and recall (0.64)

🌐 Web Application Features

🎨 User Interface Design

• Modern Healthcare UI: Professional color scheme with medical theme
• Responsive Layout: Three-column design optimized for various screen sizes
• Interactive Forms: User-friendly input controls with validation
• Real-time Feedback: Instant prediction results with visual indicators

🔧 Core Functionalities

1. Patient Data Input

• 21 comprehensive health indicator inputs
• Dropdown selections for categorical variables
• Numeric inputs with validation ranges
• User-friendly labels and descriptions

2. Risk Prediction Engine

# Prediction Process
input_data → preprocessing → scaling → model_prediction → risk_classification

3. Visualization Dashboard

• Prediction Probabilities: Bar chart showing confidence levels
• Feature Importance: Interactive chart of decision factors
• Risk Assessment: Color-coded risk level indicators

4. Professional Styling

/* Custom Healthcare Theme */
- Primary Color: #4CAF50 (Medical Green)
- Background: #f0f2f6 (Clean White-Gray)
- Accent Colors: Professional medical palette
- Typography: Clear, readable medical fonts

🚀 Installation & Setup

Prerequisites

Python 3.12+
pip (Python package manager)
Git (for version control)

Step-by-Step Installation

1. Clone the Repository

git clone https://github.com/your-username/predictive-model-for-assessing-the-risk-of-disease.git
cd predictive-model-for-assessing-the-risk-of-disease

2. Create Virtual Environment

# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Verify Installation

# Check if all models are present
ls *.joblib
# Expected output:
# logistic_regression_model.joblib
# scaler.joblib

5. Run the Application

streamlit run app.py

The application will be available at: http://localhost:8501

💡 Usage

For Healthcare Professionals

1. Patient Assessment

1. Launch the web application
2. Input patient's health indicators
3. Click "Predict Health Risk"
4. Review the risk classification and probability scores
5. Use feature importance chart to understand key risk factors

2. Clinical Decision Support

• No Risk: Routine monitoring, lifestyle counseling
• Mild Risk: Increased monitoring, preventive interventions
• Severe Risk: Immediate clinical evaluation, intensive management

For Researchers

1. Model Development

# Explore Jupyter notebooks
jupyter notebook "Health Risk Prediction Model_Logistic Regression.ipynb"
jupyter notebook "Health Risk Prediction Model(Random Forest Classifier) (1).ipynb"
jupyter notebook "Health Risk Prediction Model( Gradient Boosting Classifier) (1).ipynb"

2. Model Comparison

• Compare accuracy scores across different algorithms
• Analyze feature importance variations
• Evaluate ROC curves and confusion matrices

📁 Project Structure

📁 predictive-model-for-assessing-the-risk-of-disease/
├── 🌐 app.py                              # Streamlit web application
├── 📊 diabetes_012_health_indicators.xls  # Training dataset
├── 📓 Health Risk Prediction Model_Logistic Regression.ipynb
├── 📓 Health Risk Prediction Model( Gradient Boosting Classifier) (1).ipynb
├── 📓 Health Risk Prediction Model(Random Forest Classifier) (1).ipynb
├── 🤖 logistic_regression_model.joblib    # Trained production model
├── ⚙️ scaler.joblib                       # Data preprocessing scaler
├── 🚀 Procfile                           # Heroku deployment configuration
├── 📦 requirements.txt                    # Python dependencies
├── 📖 README.md                          # Project documentation
├── 📁 .git/                             # Git version control
└── 📁 .idea/                            # IDE configuration files

📊 Model Outputs & Visualizations

1. 🎯 Confusion Matrix Analysis

Logistic Regression Confusion Matrix

Predicted    No Risk  Mild Risk  Severe Risk
Actual
No Risk        33,579      892        875
Mild Risk         971      983      2,677
Severe Risk     1,905    1,355      7,499

Interpretation:

• True Negatives (No Risk): 33,579 correctly identified
• True Positives (Severe Risk): 7,499 correctly identified
• False Positives: Minimal misclassification of healthy patients
• False Negatives: Some high-risk patients classified as lower risk

2. 📈 ROC Curve Analysis

# ROC Curve Performance
AUC Score: ~0.85 (Excellent discrimination ability)

Clinical Significance:

• AUC > 0.8: Excellent predictive performance
• High Sensitivity: Good at identifying actual diabetes risk
• High Specificity: Good at correctly identifying low-risk patients

3. 🔍 Feature Importance Rankings

Top 10 Most Important Features

1. General Health (GenHlth) - Overall health assessment
2. BMI - Body Mass Index (obesity indicator)
3. High Blood Pressure (HighBP) - Cardiovascular risk factor
4. Age - Age-related diabetes risk
5. High Cholesterol (HighChol) - Metabolic risk factor
6. Physical Health (PhysHlth) - Physical health issues
7. Income - Socioeconomic health determinant
8. Heart Disease or Attack - Cardiovascular comorbidity
9. Education - Health literacy indicator
10. Difficulty Walking (DiffWalk) - Physical mobility indicator

4. 📊 Model Accuracy Visualization

# Accuracy Comparison Bar Chart
Models = ['Logistic Regression', 'Random Forest', 'Gradient Boosting']
Accuracy = [84.83%, 84.11%, ~85%]

5. 🎨 Prediction Probability Distribution

The model outputs probability scores for each risk class:

• Class 0 (No Risk): Probability of no diabetes risk
• Class 1 (Mild Risk): Probability of mild diabetes risk
• Class 2 (Severe Risk): Probability of severe diabetes risk

Example Output:

Patient Risk Assessment:
├── No Risk: 15%
├── Mild Risk: 25%
└── Severe Risk: 60% ← Final Prediction

🚀 Deployment

Heroku Deployment (Production Ready)

1. Deployment Configuration

# Procfile
web: streamlit run app.py

# Runtime Requirements
Python 3.12
Streamlit 1.37.0
All dependencies in requirements.txt

2. Deployment Steps

# Login to Heroku
heroku login

# Create new app
heroku create diabetes-risk-predictor

# Deploy
git push heroku main

# Open application
heroku open

3. Environment Variables

# Set production configurations
heroku config:set ENVIRONMENT=production
heroku config:set MODEL_PATH=logistic_regression_model.joblib
heroku config:set SCALER_PATH=scaler.joblib

Local Development Server

# Run locally with hot reload
streamlit run app.py --server.runOnSave true

🔮 Future Enhancements

🤖 Machine Learning Improvements

• Deep Learning Models: Neural networks for complex pattern recognition
• Ensemble Methods: Combine multiple models for improved accuracy
• Real-time Learning: Update models with new patient data
• Explainable AI: Advanced model interpretability features

🌐 Application Features

• Multi-language Support: Localization for global healthcare
• Mobile Application: Native iOS/Android apps
• EMR Integration: Electronic Medical Record system connectivity
• API Development: RESTful API for third-party integrations

📊 Analytics & Monitoring

• Usage Analytics: Track application usage patterns
• Model Performance Monitoring: Continuous model evaluation
• A/B Testing: Compare different model versions
• Patient Feedback System: Collect outcome data for improvement

🔒 Security & Compliance

• HIPAA Compliance: Healthcare data privacy standards
• Data Encryption: End-to-end data protection
• User Authentication: Secure login system
• Audit Logging: Comprehensive activity tracking

🤝 Contributing

Development Workflow

1. Fork the Repository
2. Create Feature Branch: git checkout -b feature/new-model
3. Make Changes: Implement new features or improvements
4. Test Thoroughly: Ensure all tests pass
5. Submit Pull Request: Detailed description of changes

Contribution Areas

• Model Development: New algorithms and improvements
• UI/UX Enhancement: Better user interface design
• Documentation: Improve project documentation
• Testing: Add comprehensive test coverage
• Performance Optimization: Speed and efficiency improvements

Code Standards

# Follow PEP 8 style guidelines
# Use type hints for function parameters
# Include docstrings for all functions
# Write unit tests for new features
# Update documentation for changes

📜 License

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

🙏 Acknowledgments

• Dataset Source: CDC Behavioral Risk Factor Surveillance System
• Machine Learning Libraries: scikit-learn, pandas, numpy
• Web Framework: Streamlit for rapid application development
• Visualization: matplotlib and seaborn for data visualization
• Healthcare Community: For feedback and validation

📞 Contact & Support

• Project Maintainer: Sabareesh S P
• Email: sabareeshsp7@gmail.com

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🏆 Project Highlights

✅ Production Ready: Deployed web application with professional UI
✅ High Accuracy: 84.83% accuracy in diabetes risk prediction
✅ Comprehensive: 21 health indicators for thorough assessment
✅ Scalable: Modular architecture for easy maintenance
✅ Well Documented: Complete documentation and code comments
✅ Multiple Models: Comparison of different ML algorithms
✅ Healthcare Focused: Designed for medical professionals
✅ Open Source: MIT license for community contribution

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This project demonstrates the power of machine learning in healthcare, providing an accessible tool for diabetes risk assessment that can potentially save lives through early detection and intervention.