Table 1.
Demographic characteristics across different weight categories.
Table 2.
Multivariable logistic regression analysis of factors associated with overweight and obesity.
Table 3.
Comparison of evaluation performance between random forest and XGBoost.
Fig 1.
Importance ranking of factors influencing youth obesity with Random Forest and XGBoost models.
Fig 2.
Beeswarm plot of SHAP values for the top 15 predictors of adolescent obesity in the XGBoost model. (Note: Each point represents an individual participant. The x-axis shows the SHAP value (impact on log-odds of obesity), where positive values indicate increased risk and negative values indicate reduced risk. The y-axis lists predictors ranked by overall importance, and point colour reflects the feature value (yellow = high, purple = low). The least influential variable (Cigarettes) was excluded).
Fig 3.
Nomogram of predictors of adolescent obesity. (Note: Each predictor is shown as a horizontal scale, with values mapped to points according to their relative contribution. The “Points” axis at the top indicates the score assigned to each predictor level. Summing all scores yields a “Total Points” value, which is then projected onto the “Linear Predictor” and “Predicted Value” axes at the bottom to estimate the probability of obesity. Higher total points correspond to greater predicted obesity risk).