Beyond BMI: Machine learning approaches outperforming state-of-the-art body fat prediction

Quetelet's index, known today as BMI, has been a standard in health studies since its rise to popularity in the 1970s by Ancel Keys, with its relevance underscored by its presence in millions of Google Scholar entries. However, the BMI is often critiqued for its limited predictive accuracy for body fat percentage (PBF) due to variations across genders and ethnicities. Another criticism has been the use of kg/m² units, which is not three-dimensional like density, nor an actual ‘index’ (dimensionless) measure like that of PBF.

In recent research, sex-specific, dimensionless indices were produced using over 12,000 samples from the National Health and Nutrition Examination Survey (NHANES) and assessed across Mexican-American, European-American and African-American populations. The method was assessed in terms of overall error and classification of obesity at different body fat thresholds for males and females.

Our methods utilised a means of searching for forms of each variable measurement (waist, height, neck, etc.) that were maximally correlated with the percentage of body fat and then utilising those variables in predicting body fat. Limitations were in place such that only feasible models were produced, given that they are used by unspecialized general practitioners who must take measurements within a normal 15-minute appointment.

Our results showed our method categorically outperformed body fat prediction on all state-of-the-art approaches considered in both weighted root mean square error and obesity classification.

As a result, adopting our models can lead to a more accurate prediction of a person’s body fat percentage without needing scans such as dual X-ray absorptiometry. An early and more accurate body fat prediction can lead to earlier and less expensive detection of fat-related health problems.