ArticleBMC medical informatics and decision making2023
Mitigating underreported error in food frequency questionnaire data using a supervised machine learning method and error adjustment algorithm.
Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Empirically derived dietary patterns and prostate cancer risk: a systematic review and meta-analysis of prospective cohorts.European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP) · 2026Pooled it
- Gut microbiome changes and cancer immunotherapy outcomes associated with dietary interventions: a systematic review of preclinical and clinical evidence.Journal of translational medicine · 2025Pooled it
- Observational
- Closing the dietary fibre gap - developing a novel dietary fibre screening tool (SCREEN-IT) for the UK population: validity, reproducibility and usability insights.European journal of nutrition · 2026Article
- Digital and Technology-Enabled Approaches in Dietary Assessment: Addressing Bias, Error, and Feasibility in Population- and Community-Based Research.Advances in nutrition (Bethesda, Md.) · 2026Review
- Observational
- Bias-reduced Regression Analysis in Nutrition (BRAiN) index: self-reported diet and predicted cognitive function.Frontiers in nutrition · 2026Article
- Food Preservatives and the Rising Tide of Early-Onset Colorectal Cancer: Mechanisms, Controversies, and Emerging Innovations.Foods (Basel, Switzerland) · 2025Review
- How Important Are Dietary Habits Compared to Other Factors for Sleep Quality?-An Analysis Using Data from a Specific Region in Japan.Nutrients · 2025Article
- Easily Implementable Dietary Improvement Using Recipes: Analytical Method Applied to a Specific Region in Japan.Nutrients · 2025Article
- Relationship Between Dietary Habits and Stress Responses Exerted by Different Gut Microbiota.Nutrients · 2025Article
- Dietary Supplement Use in Competitive Spanish Football Players and Differences According to Sex.Nutrients · 2025Article
- Dietary patterns and chronic prostatitis: a symptom severity prediction model based on nutritional clustering and machine learning.Frontiers in nutrition · 2025Article
- Associations Between Body Image, Eating Behaviors, and Diet Quality Among Young Women in New Zealand: The Role of Social Media.Nutrients · 2024Article
- The future backbone of nutritional science: integrating public health priorities with system-oriented precision nutrition.The British journal of nutrition · 2024Review
- Changes in eating habits and lifestyle during the first year of the COVID-19 pandemic across metropolitan regions in Brazil and Germany: A survey-based cross-sectional study.Food science & nutrition · 2024Article
- Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors.Healthcare (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundFood 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.
methodIn 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.
resultAccording to the results, we have high model accuracies ranging from 78% to 92% in participant collected data and 88% in simulated data.
conclusionThis 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.