Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study

We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. In this study, mortality models were built using gradient boosting decision trees...

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Detalles Bibliográficos
Autores principales: Madakkatel, Iqbal, Zhou, Ang, McDonnell, Mark D., Hyppönen, Elina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626442/
https://www.ncbi.nlm.nih.gov/pubmed/34837000
http://dx.doi.org/10.1038/s41598-021-02476-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626442/
https://www.ncbi.nlm.nih.gov/pubmed/34837000
http://dx.doi.org/10.1038/s41598-021-02476-9

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