Previously submitted to: JMIR Medical Informatics (no longer under consideration since Nov 05, 2022)
Date Submitted: Feb 28, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Development and Validation of a Prediction Equation for Body Fat Percentage from Measured BMI: A Machine Learning Approach
ABSTRACT
Background:
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.
Objective:
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.
Methods:
We used data from 5,931 and 2,340 adults aged 20 to 69 in NHANES 1999-2002 and NHANES 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 R2 and root mean square error. We compared our findings with other published models and utilized our best models to assess the amount of bias in the association between predicted body fat and elevated low-density lipoprotein (LDL).
Results:
Three models included BMI, BMI2, age, gender, education, income and interaction terms and produced R-squared values of 0.86 and yielded the smallest standard errors of estimation. The amount of bias in the association between predicted BF% and elevated LDL from our best model was -0.005.
Conclusions:
Our models provided strong predictive abilities and low bias compared to most published models. Its strengths rely on its simplicity and its ease of use in low-resource settings.
Citation
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