🏥 Hospital Readmission Risk Predictor

An end-to-end ""machine learning + Streamlit"" application that predicts ""30-day hospital readmission risk"" using patient encounter data and presents results through an interactive dashboard.

This project demonstrates ""real-world ML deployment"", feature engineering, and decision-support thinking for healthcare analytics.

🚀 Live Demo

👉 https://hospital-readmission-predictor-kro4jvk9njwzt3a4xitsbj.streamlit.app/

📌 Key Features

- Predicts probability of 30-day hospital readmission
- Interactive Streamlit dashboard with real-time inputs
- Trained ""Random Forest Classifier"" with class imbalance handling
- Clear risk categorization (High Risk vs Low Risk)
- Estimated cost savings insight for high-risk prevention
- Production-ready project structure

🧠 Machine Learning Overview

- Model: RandomForestClassifier
- Class Weighting: Balanced
- Evaluation Focus: Recall & probability-based decision threshold
- Target: Hospital readmission within 30 days

Model Features Used:

- `age_numeric`
- `time_in_hospital`
- `num_lab_procedures`
- `num_medications`
- `number_emergency`
- `number_inpatient`

🖥️ Tech Stack

- Python
- Pandas, NumPy
- Scikit-learn
- Streamlit
- Joblib
- Jupyter Notebook

📁 Project Structure

Hospital-Readmission-Predictor/
│
├── app/
│   └── app.py              # Streamlit application
│
├── models/
│   └── rf_readmission_model.pkl
│
├── notebooks/
│   └── 01_data_exploration.ipynb
│
├── data/                   # Sample / reference data
│
├── requirements.txt
├── .gitignore
└── README.md

⚙️ How to Run Locally

# 1. Clone the repository
git clone https://github.com/YashJadhav100/Hospital-Readmission-Predictor.git

# 2. Navigate to project
cd Hospital-Readmission-Predictor

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run Streamlit app
streamlit run app/app.py

📊 Use Case

Healthcare providers can use this tool to:

Identify high-risk patients early

Optimize discharge planning

Reduce avoidable readmissions

Improve patient outcomes

Estimate potential cost savings

👤 Author

Yash Jadhav
Graduate Student – Computer Science
Syracuse University

🔗 GitHub: https://github.com/YashJadhav100

🔗 LinkedIn: https://www.linkedin.com/in/yashvjadhav/

📄 Disclaimer

This project is for educational and analytical purposes only and does not constitute medical advice.