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| Main Author: | |
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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.19143920 |
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Table of Contents:
- <p><span><span><span dir="auto">This study presents a machine learning–based predictive framework for identifying patients at high risk of 30-day hospital readmission among individuals with diabetes. Using the UCI Diabetes dataset comprising over 101,000 hospital encounters, we developed and evaluated Random Forest and XGBoost models, achieving a recall of 76% at an optimized threshold. Feature importance and SHAP analysis identified key predictors including prior inpatient visits, medication burden, and length of stay. Building on these findings, we propose a CDC HI-5 aligned intervention incorporating risk stratification, care coordination, medication management, and a real-time clinical dashboard. The results demonstrate the feasibility of integrating predictive analytics into population health strategies to reduce preventable readmissions.</span></span></span></p>