Fig. 2.

Exploratory analysis of the correlation between fat-related indicators and clinical outcomes. a The LOWESS curves depict the associations between fat-related indicators and clinical outcomes, while the box diagram reveals variations in clinical outcomes observed across quintiles of the indicators. b Curves from the restricted cubic spline regression depicting various fat-related indicators and their impact on clinical outcomes. c ROC curves for fat-related metrics in predicting clinical outcomes and ROC curves for machine learning-based methods in prognosticating clinical outcomes. d Ranking the importance of various obesity indicators in Gradient Boosting Classifier machine learning models. e Summary plot of SHAP values for the predictive features in the Gradient Boosting Classifier model. In the SHAP summary plot, each row represents a feature, and each point represents a sample. The color denotes the magnitude of the feature value, with red indicating high values and blue indicating low values. Positive SHAP values indicate a positive impact of the feature on the model, while negative values indicate a negative impact. Abbreviations: SHAP, Shapley’s additive interpretation