Development and validation of a prediction equation for body fat percentage from measured BMI: a supervised machine learning approach.

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 R² and root

SUBMITTER: Xu S 

PROVIDER: S-EPMC10192430 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature