Healthcare ML Analytics Dashboard

A comprehensive machine learning project for healthcare data analysis featuring breast cancer classification and diabetes risk prediction models with an interactive web dashboard.

Live Demo

Try the Live Application:

• Streamlit App: Healthcare ML Dashboard
• Repository: GitHub Repository

Project Overview

This repository contains a complete healthcare machine learning solution with two main components:

1. Jupyter Notebook Analysis - Detailed exploratory data analysis and model development
2. Streamlit Web Application - Interactive dashboard for real-time predictions

Repository Structure

healthcare-ml-dashboard/
├── Healthcare_ML_Models_Annotated_and_Visualized.ipynb  # Main analysis notebook
├── Healthcare_ML_Models_Organized.ipynb                 # Clean organized code
├── healthcare_app.py                                    # Streamlit web application
├── requirements.txt                                     # Python dependencies
├── resources/                                           # Images and assets
│   ├── README.md                                       # Image guidelines
│   ├── header_image.jpg                                # Dashboard header image
│   ├── breast_cancer_model.jpg                         # Breast cancer section image
│   ├── diabetes_model.jpg                              # Diabetes section image
│   └── data_exploration.jpg                            # Data exploration image
├── .gitignore                                          # Git ignore file
└── README.md                                           # This file

Quick Start

Prerequisites

• Python 3.7 or higher
• pip package manager

Local Installation

1. Clone or Download the Repository

   git clone https://github.com/Uday2kranth/Health-care-ML-Final-project.git
   cd Health-care-ML-Final-project

2. Install Dependencies

   pip install -r requirements.txt

3. Run the Streamlit App

   streamlit run healthcare_app.py

4. Open in Browser

   • The app will automatically open at http://localhost:8501
   • If not, manually navigate to the URL shown in the terminal

Cloud Deployment Options

Streamlit Cloud (Recommended)

1. Fork this repository
2. Sign up at Streamlit Cloud
3. Connect your GitHub repository
4. Deploy with one click
5. Live app: Healthcare ML Dashboard

Other Deployment Options

• Railway: Simple deployment with GitHub integration
• Render: Free tier available for small projects
• Google Cloud Platform: Professional deployment option
• AWS: Enterprise-level deployment solution

Healthcare_ML_Models_Annotated_and_Visualized Notebook

This comprehensive Jupyter notebook provides a step-by-step analysis of healthcare datasets:

Step 1: Data Loading and Exploration

• Breast Cancer Dataset: 569 samples, 30 features
• Diabetes Dataset: 442 samples, 10 features
• Initial data inspection and basic statistics

Step 2: Exploratory Data Analysis (EDA)

• Statistical Summary: Mean, median, standard deviation for all features
• Class Distribution: Visualization of target variable distributions
• Feature Correlations: Correlation heatmaps to identify relationships
• Data Visualization:
  • Histograms for feature distributions
  • Box plots for outlier detection
  • Scatter plots for feature relationships

Step 3: Data Preprocessing

• Missing Value Analysis: Check for null values
• Feature Selection: Identify most important features
• Data Scaling: Normalize features for better model performance
• Train-Test Split: Prepare data for model training

Step 4: Model Development

Breast Cancer Classification Model

• Algorithm: XGBoost Classifier
• Features Used:
  • Worst radius
  • Worst perimeter
  • Mean concave points
  • Worst concave points
  • Worst area
• Performance Metrics:
  • Accuracy: ~95%
  • Precision, Recall, F1-Score
  • Confusion Matrix

Diabetes Regression Model

• Algorithm: XGBoost Regressor
• Features Used: All 10 diabetes features
• Performance Metrics:
  • R² Score: ~0.5
  • Mean Squared Error
  • Mean Absolute Error

Step 5: Model Visualization

• Feature Importance Plots: Bar charts showing feature contributions
• Confusion Matrix: Visual representation of classification results
• Prediction vs Actual: Scatter plots for regression analysis
• ROC Curves: Model performance evaluation

Step 6: Model Evaluation

• Cross-validation: Ensure model robustness
• Performance Analysis: Detailed metrics interpretation
• Model Comparison: Baseline vs optimized models

Healthcare App (Streamlit Dashboard)

The interactive web application provides a user-friendly interface for healthcare predictions:

How the App Works

1. Application Structure

# Main components
├── load_and_prepare_data()     # Data loading and caching
├── train_breast_cancer_model() # Model training with caching
├── train_diabetes_model()      # Model training with caching
├── load_image_from_resources() # Image loading functionality
└── main()                      # Main application logic

2. Sidebar Navigation

• Model Selection: Choose between three main sections
• Project Information: Overview of technologies used
• Responsive Design: Clean, professional interface

3. Main Sections

Breast Cancer Classification

• Input Interface: Interactive sliders for patient measurements
• Real-time Prediction: Instant classification results
• Confidence Scores: Probability of benign vs malignant
• Visual Analytics: Feature importance charts

Diabetes Risk Analysis

• Health Metrics Input: Patient health parameter sliders
• Risk Assessment: Continuous risk score calculation
• Performance Visualization: Actual vs predicted scatter plots
• Risk Interpretation: Low, moderate, high risk categories

Data Exploration

• Dataset Overview: Summary statistics and distributions
• Interactive Charts: Pie charts, histograms, correlation heatmaps
• Comparative Analysis: Side-by-side dataset comparison

4. Technical Features

• Caching: @st.cache_data and @st.cache_resource for performance
• Responsive Layout: Multi-column layouts and tabs
• Error Handling: Graceful error management
• Image Support: Dynamic image loading from resources folder

UI/UX Features

• Modern Design: Gradient backgrounds and professional styling
• Interactive Elements: Sliders, buttons, and dropdowns
• Visual Feedback: Color-coded prediction results
• Responsive Layout: Works on different screen sizes

Step-by-Step Usage Instructions

For Repository Users (Fork/Download)

1. Environment Setup

# After cloning/downloading
git clone https://github.com/Uday2kranth/Health-care-ML-Final-project.git
cd Health-care-ML-Final-project
pip install -r requirements.txt

2. Running the Jupyter Notebook

# Start Jupyter
jupyter notebook

# Open in browser
# Navigate to Healthcare_ML_Models_Annotated_and_Visualized.ipynb
# Run cells sequentially from top to bottom

3. Running the Streamlit App

# Start the web application
streamlit run healthcare_app.py

# Access in browser
# Navigate to http://localhost:8501

4. Deploying Your Own Version

Quick Deploy to Streamlit Cloud

1. Fork this repository to your GitHub account
2. Sign up at Streamlit Cloud
3. Connect your GitHub repository
4. Click "Deploy app"
5. Live app: Healthcare ML Dashboard

Alternative Deployment Options

• Railway: Simple deployment with GitHub integration
• Render: Free tier available for small projects
• Google Cloud Platform: Professional deployment option
• AWS: Enterprise-level deployment solution

4. Using the Dashboard

1. Select Model from sidebar:

   • Breast Cancer Classification
   • Diabetes Risk Analysis
   • Data Exploration

2. Input Data using interactive sliders:

   • Adjust values based on patient measurements
   • See real-time updates

3. Get Predictions:

   • Click prediction buttons
   • View confidence scores and risk levels
   • Analyze visualization charts

4. Explore Data:

   • Review dataset statistics
   • Examine feature correlations
   • Compare model performances

5. Customization Options

Adding Images

# Add images to resources folder
resources/
├── header_image.jpg          # Main dashboard header
├── breast_cancer_model.jpg   # Breast cancer section
├── diabetes_model.jpg        # Diabetes section
└── data_exploration.jpg      # Data exploration section

Modifying Models

• Edit train_breast_cancer_model() for different features
• Adjust train_diabetes_model() for different algorithms
• Update visualization functions for custom charts

Technical Stack

Machine Learning

• Scikit-learn: Data preprocessing and metrics
• XGBoost: Gradient boosting models
• Pandas: Data manipulation
• NumPy: Numerical operations

Visualization

• Plotly: Interactive charts
• Matplotlib: Static plots
• Seaborn: Statistical visualizations

Web Framework

• Streamlit: Web application framework
• PIL (Pillow): Image processing

Development Tools

• Jupyter: Interactive development
• Git: Version control

Model Performance

Breast Cancer Classification

• Accuracy: ~95%
• Precision: High precision for both classes
• Recall: Balanced recall scores
• Features: 5 most important features selected

Diabetes Risk Prediction

• R² Score: ~0.5
• MSE: Reasonable prediction error
• Feature Importance: All 10 features contribute
• Range: Risk scores from 25-346

Troubleshooting

Common Issues

1. Import Errors

   # Ensure all packages are installed
   pip install -r requirements.txt

2. Port Already in Use

   # Use different port
   streamlit run healthcare_app.py --server.port 8502

3. Image Loading Issues

   # Check resources folder exists
   # Verify image file names match exactly

Contributing

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

Deployment Links

Live Application:

• Primary: Healthcare ML Dashboard - Streamlit Cloud Deployment
• Repository: GitHub Repository - Source Code

Deployment Status:

• 

License

This project is for educational purposes. Please ensure compliance with healthcare data regulations in production use.

Acknowledgments

• Scikit-learn for providing healthcare datasets
• Streamlit for the web framework
• XGBoost for high-performance machine learning
• Plotly for interactive visualizations

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Important Note: This application is for educational and demonstration purposes only. Always consult qualified healthcare professionals for medical advice and diagnosis.

Support

For questions or issues:

1. Check the troubleshooting section
2. Review the code documentation
3. Open an issue on the repository
4. Consult the official documentation for used libraries

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Happy Healthcare Analytics!