A machine learning-driven and explainable prediction model for in-hospital mortality in patients with diabetes mellitus and congestive heart failure: integrating XGBoost and SHAP approaches.
Overview & Synopsis
Open access biomedical research article published in Front Public Health.
Indexed from repository: PubMed Central
(
pmc.ncbi.nlm.nih.gov)
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Document Information
| Document Title | A machine learning-driven and explainable prediction model for in-hospital mortality in patients with diabetes mellitus and congestive heart failure: integrating XGBoost and SHAP approaches. |
|---|---|
| Author / Organization | Hu J, Su S, Liu D |
| Subject & Discipline | Front Public Health |
| Categories | Medicine & Health |
| Publication Year | 2026 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
PubMed Central
(pmc.ncbi.nlm.nih.gov)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-85418 |
| Original Source Link | Visit Source Page on pmc.ncbi.nlm.nih.gov » |