Enhancing Cardiovascular Risk Prediction: Development of an Advanced Xgboost Model with Hospital-Level Random Effects
Background: Ensemble tree-based models such as Xgboost are highly prognostic in cardiovascular medicine, as measured by the Clinical Effectiveness Metric (CEM). However, their ability to handle correlated data, such as hospital-level effects, is limited. Objectives: The aim of this work is to develo...
I tiakina i:
| Ngā kaituhi matua: | , , , , , , , , |
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| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
MDPI AG
2024-10-01
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| Rangatū: | Bioengineering |
| Ngā marau: | |
| Urunga tuihono: | https://www.mdpi.com/2306-5354/11/10/1039 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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