Gender-based approach to estimate the human body fat percentage using Machine Learning

Keeping a certain balance of body fat is essential for a healthy life, and proper nutrition is fundamental. One of the most worrying malnutrition problems is obesity, which plays a significant risk factor for chronic diseases like cardiovascular diseases, diabetes, and cancer. The Dual-energy X-ray absorptiometry (DXA) is the most accurate and automatic method that returns the body fat percentage; however, this method is expensive and not easily found at clinics. A lower-cost way of estimating the body fat percentage is through anthropometric measures. However, the literature has shown that estimating body fat percentage on women is challenging. In this work, we propose an approach specialized in gender to estimate body fat percentage using machine learning. Another contribution of this work is a dataset, BodyFat-163 (BF-163), containing the 12 anthropometric measures and the body fat percentage from DXA exams collected by a specialist. The dataset consists of 163 individuals (84 males and 79 females). Our experiments involved a variety of methods of regression, which includes Random Forest Regression, Extreme Gradient Boosting, Decision Tree, Support Vector Regression, Multilayer Perceptron Regression, and Least Square Support Vector Regression. The experiment results were evaluated with the metrics Mean Absolute Error (MAE), Root Mean Square Error, Mean Squared Logarithmic Error, and R-2 score. Our gender-based approach successfully estimates the body fat percentage achieving a MAE = 2:756, and R-2 = 0:68 on the male set and MAE = 3:869, and R-2 = 0:69 on the female set.