Mitigating underreported error in food frequency questionnaire data using a supervised machine learning method and error adjustment algorithm

Mitigating underreported error in food frequency questionnaire data using a supervised machine learning method and error adjustment algorithm

Abstract Background Food frequency questionnaires (FFQs) are one of the most useful tools for studying and understanding diet-disease relationships. However, because FFQs are self-reported data, they are susceptible to response bias, social desirability bias, and misclassification. Currently, severa...

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Bibliographic Details
Main Authors: Anjolaoluwa Ayomide Popoola, Jennifer Koren Frediani, Terryl Johnson Hartman, Kamran Paynabar
Format: Article
Language:English
Published: BMC 2023-09-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Food frequency questionnaire
Machine learning
Supervised learning
Underreporting
Measurement error
Error adjustment model
Online Access:https://doi.org/10.1186/s12911-023-02262-9

Internet

https://doi.org/10.1186/s12911-023-02262-9

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