Diabetes Patient Risk Analytics

Identifying clinical drivers for hospital readmissions in diabetic patients to improve patient outcomes using Python and Tableau.

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📊 Project Overview

This project analyzes a dataset representing 10 years (1999-2008) of clinical care at 130 US hospitals. The goal is to identify factors that lead to high 30-day readmission rates among diabetic patients, providing actionable insights for hospital administrators to improve transition-of-care protocols.

🛠️ Tech Stack

• Language: Python 3.x
• Libraries: Pandas (Data Wrangling), Seaborn/Matplotlib (Visualization), NumPy
• Business Intelligence: Tableau (Interactive Dashboard)

📈 Key Insights (Work in Progress)

• Insight 1: [e.g., Patients aged 70+ show a 15% higher readmission rate]
• Insight 2: [e.g., Specific medication changes correlate with lower bounce-back rates]

🧹 Data Processing Highlights

To ensure high-quality analysis, the following steps were taken:

1. Handling Missing Values: Replaced ? placeholders with NaN and assessed column integrity.
2. De-duplication: Filtered for the first encounter per patient to prevent data leakage.
3. Feature Categorization: Grouped ICD-9 diagnosis codes into clinical categories (e.g., Circulatory, Respiratory).

🚀 How to Use

1. Clone the repository.
2. Install dependencies: pip install pandas matplotlib seaborn.
3. Open analysis.ipynb to view the step-by-step EDA.

⚖️ Ethical Considerations

This project uses an anonymized, public dataset. In a real-world setting, this analysis would be performed in compliance with HIPAA regulations to ensure patient privacy.

🤝 Acknowledgements

This project was developed with the assistance of Gemini (Google AI), which served as a technical collaborator for:

• Architecting the repository structure and documentation.
• Refining data cleaning strategies for healthcare-specific datasets.
• Debugging and optimizing Python analysis workflows.