# Interpretable machine learning for identifying adolescent obesity risk and identifying key determinants

## TL;DR

This study uses interpretable machine learning to identify factors and risk thresholds for adolescent obesity, focusing on behaviors and environments.

## Contribution

The novel use of interpretable ML to identify specific risk thresholds and key predictors for adolescent obesity.

## Key findings

- The LightGBM model achieved the highest accuracy (0.8788) in classifying adolescent obesity.
- Sedentary behavior was identified as the most significant predictor of adolescent obesity.
- Key risk thresholds include sedentary time exceeding 5 hours on weekends and birth weight over 4.0 kg.

## Abstract

This study utilizes interpretable machine learning to identify and prioritize key associated factors for adolescent obesity across individual, family, and school domains, as well as to establish specific risk thresholds that can inform targeted interventions.

Data were obtained from the China Education Panel Survey (CEPS), which included 7,397 adolescents. Six ML models (SVM, XGBoost, LightGBM, LR, RF, MLP) were developed and evaluated. The best-performing model was interpreted using SHAP analysis to assess feature contributions.

The LightGBM model demonstrated the highest accuracy (0.8788). This study primarily focused on the accurate classification of adolescent obesity status within a clinical decision-making context. Consequently, accuracy was prioritized as the key metric for directly assessing the model’s overall classification performance. Key predictors of this model sedentary time, school ranking, academic workload, birth weight, body image, family economic status, school location, household registration, and physical activity. Among these, sedentary behavior emerged as the most significant predictor. Specific risk thresholds were identified, including sedentary time exceeding 5 h on weekends and birth weight greater than 4.0 kg.

This study underscores the utility of interpretable ML in identifying key predictors associated with adolescent obesity. The findings suggest that interventions might prioritize reducing sedentary behavior, the moderation of academic workload, and the enhancement of body image perception. Additionally, family and school environments play crucial roles in the prevention of obesity.