Development of Body Fat Estimation Equations Based on BMI, Age, Gender, and Ethnic Groups

Body fat (BF) percentage is a measurement of human health. The simple predictive equation between BF and the anthropometric measurement helps evaluate the BF value. The BMI value, weight divided by the height square, has been used as the significant factor in BF values. However, other factors involving age, gender, and ethnicity may also affect the BF values. An adequate model considering all influencing factors is critical in predicting the BF value. Many empirical equations have been proposed to evaluate these factors. This study uses previously collected data to establish the BF equation with modern regression analysis. Three forms of body mass index (BMI), BMI, logarithmic BMI, and inverse BMI, are selected as independent valuables. The other variables include age, gender, and ethnicity. The t-value was used to test the significant influence of each variable in the regression equations. The Prediction Sum of Squares (PRESS) statistics were used to evaluate the models' predictive ability. Categorical testing was adopted to evaluate the significant influence of these variables of age, gender, and ethnicity. The results of this study indicated that the best BF model involved BMI, BMI2, age, and age2 variables. The age, gender and ethnicity tested by categorical test significantly affected BF values. No single form of the BF equation can be proposed to represent all ethnicities or both genders. Modern regression analysis can provide more scientifically based model-building techniques than machine learning. By calculating the BMI value, the cutoff point of BF values needs to consider the difference between gender and ethnicity. The regression technique in this study provides a reasonable method to establish the BF equation for different ethnicities and other factors.