ArticleScientific reports2023
Development and validation of a prediction equation for body fat percentage from measured BMI: a supervised machine learning approach.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Duplicate-Aware Internal Validation of Machine-Learning Models for Classifying a Tanita BIA-Derived High-Adiposity Phenotype Using Simple Anthropometric Predictors.Journal of clinical medicine · 2026Article
- Percentage Body Fat Corresponding to Korean BMI Classification for Obesity in a Nationally Representative Population.Nutrients · 2026Article
- The Association Between Body Fat Percentage and Severe Headache or Migraine: A National Cross-Sectional Survey.Cureus · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Body mass index is a widely used but poor predictor of adiposity in populations with excessive fat-free mass. Rigorous predictive models validated specifically in a nationally representative sample of the US population and that could be used for calibration purposes are needed. The objective of this study was to develop and validate prediction equations of body fat percentage obtained from Dual Energy X-ray Absorptiometry using body mass index (BMI) and socio-demographics. We used the National Health and Nutrition Examination Survey (NHANES) data from 5931 and 2340 adults aged 20 to 69 in 1999-2002 and 2003-2006, respectively. A supervised machine learning using ordinary least squares and a validation set approach were used to develop and select best models based on R
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