Interpretable machine learning framework to predict gout associated with dietary fiber and triglyceride-glucose index.
Background: Gout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) in...
| Publicado en: | Nutrition & Metabolism Vol. 21; pp. 1 - 16 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
BioMed Central
5/14/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |