Improving Dietary Data Quality in Nutrition Studies: Development and Validation of an Explainable, Reproducible, Open Machine Learning-Assisted Framework for Outlier Detection in 24-Hour Recalls

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ID: 326334
2026
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Abstract
Twenty-four-hour dietary recalls (24HRs) are widely used in nutritional epidemiology, but repeated administration is required to capture within-person variability, introducing temporal dependence and challenges for data cleaning. Existing outlier detection approaches are largely static, manual, and based on adult-centric thresholds, limiting reproducibility and scalability. We developed and validated an explainable, reproducible machine learning (ML)-assisted framework to detect and evaluate implausible intake values in cross-sectional and longitudinal 24HRs and to generate AI-ready dietary datasets. The open pipeline integrates automated statistical detection using digitized National Cancer Institute 5th-95th percentile intake ranges, longitudinal interquartile range-based flagging, and ML-based rule explainability for outlier detection. Validation was conducted using the Microbiota, Growth, and Diet (MiGrowD) study in Ontario, Canada (126 children aged 8-12 years), and data from the 2015 Canadian Community Health Survey-Nutrition, a nationally representative sample of Canadian children and adults with repeated 24HRs (n = 15 216). Across energy intake and seven nutrients, 75 recalls containing at least one potentially implausible value were identified. In longitudinal data, the framework achieved > 77% sensitivity, 97% specificity, and 88% precision, outperforming the static method. This explainable data-driven framework improves data quality, transparency, and reproducibility, supporting scalable epidemiologic analyses using AI-ready dietary data.
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openalex_W7204088660 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Paraskevi Massara, Stephanie Saab, Arooj Asrar, Jessica Omand, Laura N. Anderson, Charles Keown‐Stoneman, Jonathon L. Maguire, Robert Bandsma, Catherine S. Birken, Elena M. Comelli
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Journal american journal of epidemiology
Year 2026
DOI
10.1093/aje/kwag204
URL
https://doi.org/10.1093/aje/kwag204
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