Background and Objective: Previous studies have shown that muscle mass and visceral fat are interrelated and affect metabolic health. However, there is limited research exploring machine learning (ML) models that can help us understand the relationship between muscle mass and the risk of adiposity in the adult population. The objective of this study was to identify predictors of obesity on the basis of data from 13,663 adults assessed via body composition analysis via optimal and interpretable ML algorithms. Methods: A cross-sectional design was used to analyze data from 13,663 adults, comprising men (n = 6877) and women (n = 6786). The variables were obtained via 8-point multifrequency BIA under standardized clinical protocols with an Inbody® Model 770 device validated for the adult population. To illustrate the interaction between body composition components, a probability heatmap was generated on the basis of the values predicted from the logistic model. The decision boundary was defined via the metabolic risk probability gradient, allowing visualization of the two-dimensional transition between low- and high-risk states. Statistical processing and figure generation were performed via Python software v.3.10. Results: The evaluation of the 10 algorithms demonstrated exceptional predictive performance, with the multilayer perceptron (MLP) standing out as the superior model in both sexes. The AUC-ROC was 0.981 for men and 0.993 for women, with F1 scores of 0.912 and 0.969, respectively. Overall, systematically higher accuracy was observed in the female cohort, exceeding 95% accuracy in most models. Conclusions: Muscle mass has been shown to act as a metabolic mediator, modulating and reducing the risk associated with visceral adiposity. It also concludes that the use of ML algorithms, specifically neural networks, is a good model for analyzing the risk associated with excess visceral fat.
Yáñez-Sepúlveda et al. (Thu,) studied this question.