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, 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.

Res

SUBMITTER: Popoola AA 

PROVIDER: S-EPMC10492312 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature