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

Abstract 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 decisi...

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Autores principales: Iqbal Madakkatel, Ang Zhou, Mark D. McDonnell, Elina Hyppönen
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/8d07dd6dd43b4072bad55c2c9fa43b2b
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