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

• Type of Material: Article
• Authors: Anjolaoluwa Ayomide Popoola, Georgia Institute of Technology; Jennifer K Frediani, Emory University; Terryl Johnson Hartman, Emory University; Kamran Paynabar, Georgia Institute of Technology
• Language: English
• Date: 2023-09-09
• Publisher: BMC
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): https://doi.org/10.1186%2Fs12911-023-02262-9
• Title of Journal or Parent Work: BMC Medical Informatics and Decision Making
• Volume: 23
• Start Page: 178
• Grant/Funding Information: This collection of the dataset used in this project was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378.
• Keywords: food frequency questionnaire; measurement error; Error adjustment model; machine learning; supervised learning; underreporting
• Research Categories: Biology, Biostatistics

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.