| _version_ | 1866902001041801216 |
|---|---|
| author | Mohammed Mustafa khan |
| author_facet | Mohammed Mustafa khan |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19143920 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Predictive Risk Stratification for Preventable Diabetes Readmissions in U.S. Hospitals Mohammed Mustafa khan Diabetes Machine Learning Healthcare Analytics Hospital Readmission Population Health <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> |
| title | Predictive Risk Stratification for Preventable Diabetes Readmissions in U.S. Hospitals |
| topic | Diabetes Machine Learning Healthcare Analytics Hospital Readmission Population Health |
| url | https://doi.org/10.5281/zenodo.19143920 |