Predicting Body Fat Percentage from Simple Anthropometric Measurements: A Machine Learning Approach

Accurately assessing body fat percentage (BF%) is crucial for healthcare and fitness but is hindered by gold-standard methods that are costly and invasive. This study employs a dataset containing variables such as age, sex, Body Mass Index (BMI), and body circumferences, from individuals whose body fat percentage (BF%) was estimated via underwater weighing, to develop predictive machine learning models. Multiple regression techniques alongside a neural network were employed to compare model accuracies in estimating BF%. Ridge Regression emerged as the most effective model, demonstrating the highest R2 score. Notably, feature importance analysis using ElasticNet and SHAP revealed that abdominal circumference was the most significant predictor of BF%, challenging the adequacy of BMI as a measure of adiposity. These insights advocate for the broader adoption of circumference measurements in everyday practice to enhance the predictive accuracy of cost-effective and easily performed BF% estimation.