Akademik Veri Yönetim Sistemi
9th International Conference on Obesity and Chronic Diseases, Massachusetts, Amerika Birleşik Devletleri, 5 - 07 Kasım 2025, ss.7-8, (Özet Bildiri)
Background: Body mass index (BMI) percentiles are widely used to assess obesity in adolescents, but they often misclassify
adiposity, particularly in moderate to high-risk groups.
Objective: To derive and validate sex-specific relative BMI (rBMI) cut-offs using decision tree models to improve adolescent
adiposity classification compared to BMI percentile and receiver operating characteristic (ROC)-based rBMI methods.
Methods: We analyzed data from 567 U.S. adolescents (ages 11 - 19) with DXA-derived body fat percentage (BF%) as the
reference standard. Classification and regression tree models were used to generate sex-specific rBMI cut-offs for four adiposity
categories: normal weight, overweight, obese, and severely obese. Model performance was evaluated using accuracy, kappa, Matthew’s
correlation coefficient (MCC), sensitivity, specificity, balanced accuracy, and F1-score. Performance was benchmarked
against BMI percentile and ROC-based rBMI cut-offs.
Results: The tree-derived rBMI model achieved the highest overall accuracy (0.720), kappa (0.625), and MCC (0.627). It outperformed
alternative methods across most adiposity categories, with substantial improvements in classifying overweight (F1 =0.572), obese (F1 = 0.636), and severely obese (F1 = 0.808) adolescents. The model showed better alignment with BF% defined
categories than BMI percentile and ROC-based rBMI, which tended to underestimate high-risk individuals.
Conclusion: Tree-derived rBMI cut-offs provide a statistically and clinically robust method for classifying adolescent adiposity.
This approach enhances early identification of at-risk youth and can be integrated into pediatric screening to guide targeted
interventions.