Rising global obesity necessitates precise, personalized management of energy balance. While machine learning effectively handles complex physiological data, traditional “black-box” models lack the transparency needed to understand how specific behaviors impact metabolic outcomes. This opacity limits trust and the practical utility of these models in guiding actionable health interventions. To address this challenge, this study evaluates the effectiveness of activity features in predicting energy expenditure using four distinct machine learning paradigms: Support Vector Regression (SVR), Random Forest (RF), XGBoost, and Radial Basis Function Neural Network (RBFNN). These models were selected to compare performance across different algorithmic mechanisms (kernel-based, ensemble, and neural networks) on highly correlated datasets. Furthermore, SHAP (SHapley Additive exPlanations) analysis was integrated to visualize feature contributions and resolve the interpretability issue. Comparative analysis revealed that SVR demonstrated the strongest generalization ability, achieving an R2 of 0.78 on the test set, whereas XGBoost, despite superior training performance, suffered from overfitting. SHAP analysis identified “Total Distance” and “Total Steps” as the most critical predictive features. Crucially, the results highlighted that “Very Active Minutes” significantly amplifies energy expenditure, whereas light activity and sedentary behavior contribute minimally. This study validates the robustness of SVR for small-scale, physiological datasets and demonstrates that integrating SHAP enhances model transparency8. These findings provide a data-driven theoretical foundation for optimizing exercise plans, suggesting that personalized health interventions should prioritize increasing the intensity and distance of physical activity to maximize health outcomes.
Lin et al. (Wed,) studied this question.