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BMC Medical Informatics and Decision Making

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Open Access 01-12-2023 | Confusion | Research

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

Authors: Anjolaoluwa Ayomide Popoola, Jennifer Koren Frediani, Terryl Johnson Hartman, Kamran Paynabar

Published in: BMC Medical Informatics and Decision Making | Issue 1/2023

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, several methods have been created to combat these issues by modelling the measurement error in diet-disease relationships.

Method

In this paper, a novel machine learning method is proposed to adjust for measurement error found in misreported data by using a random forest (RF) classifier to label the responses in the FFQ based on the input dataset and creating an algorithm that adjusts the measurement error. We demonstrate this method by addressing underreporting in selected FFQ responses.

Result

According to the results, we have high model accuracies ranging from 78% to 92% in participant collected data and 88% in simulated data.

Conclusion

This shows that our proposed method of using a RF classifier and an error adjustment algorithm is efficient to correct most of the underreported entries in the FFQ dataset and could be used independent of diet-disease models. This could help nutrition researchers and other experts to use dietary data estimated by FFQs with less measurement error and create models from the data with minimal noise.

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Title: Mitigating underreported error in food frequency questionnaire data using a supervised machine learning method and error adjustment algorithm
Authors: Anjolaoluwa Ayomide Popoola
Jennifer Koren Frediani
Terryl Johnson Hartman
Kamran Paynabar
Publication date: 01-12-2023
Publisher: BioMed Central
Keyword: Confusion
Published in: BMC Medical Informatics and Decision Making / Issue 1/2023
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-023-02262-9

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Springer Medicine

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

Abstract

Background

Method

Result

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Method

Result

Conclusion

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