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The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model
An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) a...
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| Pubblicato in: | Front Public Health |
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| Autori principali: | , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Frontiers Media S.A.
2021
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8289437/ https://ncbi.nlm.nih.gov/pubmed/34291028 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fpubh.2021.680054 |
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