Frontiers in public health · 2025
Development and interpretation of a machine learning model for predicting body mass index in Chinese adolescents: a prospective cohort study.
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Zhang Zikang, Peng Wei, Sun Shaoming, Zhang Fangwen, Sun Yining, Huang Lei
Аннотация
PURPOSES: This study aimed to develop a machine learning model to predict body mass index (BMI) in adolescents based on readily accessible daily information and to investigate the influence of modifiable factors on BMI changes through model interpretation techniques.
METHODS: This study is a one-year prospective cohort study. Baseline data were collected through anthropometric measurements and questionnaires, and BMI were reassessed after 1 year. Six machine learning models were developed to predict BMI. Nested cross-validation (CV) was used for hyperparameter tuning and performance estimation. Predictors were prescreened on the inner-training folds of the nested CV using univariable analyses. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). SHapley Additive exPlanations (SHAP) was used for global and local interpretations of the models.
RESULTS: The mean BMI of the 1,827 students included in the final analysis increased from 21.18 ± 3.63 kg/m2 at baseline to 21.54 ± 3.59 kg/m2 after 1 year, with an average change of 0.36 ± 1.40 kg/m2. The CatBoost (CB) model demonstrated the best predictive performance. After calibration, it achieved an RMSE of 1.200 [95% confidence interval (CI): 1.101-1.303], MSE of 1.440 (95% CI: 1.211-1.697), MAE of 0.895 (95% CI: 0.818-0.981) and R2 of 0.902 (95% CI: 0.882-0.918). In the SHAP analysis, the top 5 modifiable features at the population level were: level of health literacy, recognize self-weight status correctly, sedentariness duration on weekends, participation in professional sports training, frequency of staying up late.
CONCLUSION: This study developed a BMI prediction model for adolescents using readily accessible daily information. The model accurately predicts BMI values 1 year later and provides both population-level and individual-level interpretability. Compared to existing studies, it offers key advantages, including independence from complex clinical data, the ability to predict continuous BMI values, and strong model interpretability. Our findings provide a promising research tool for screening high-risk adolescents, informing public health prevention and intervention strategies, and supporting personalized clinical interventions.
METHODS: This study is a one-year prospective cohort study. Baseline data were collected through anthropometric measurements and questionnaires, and BMI were reassessed after 1 year. Six machine learning models were developed to predict BMI. Nested cross-validation (CV) was used for hyperparameter tuning and performance estimation. Predictors were prescreened on the inner-training folds of the nested CV using univariable analyses. Model performance was evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). SHapley Additive exPlanations (SHAP) was used for global and local interpretations of the models.
RESULTS: The mean BMI of the 1,827 students included in the final analysis increased from 21.18 ± 3.63 kg/m2 at baseline to 21.54 ± 3.59 kg/m2 after 1 year, with an average change of 0.36 ± 1.40 kg/m2. The CatBoost (CB) model demonstrated the best predictive performance. After calibration, it achieved an RMSE of 1.200 [95% confidence interval (CI): 1.101-1.303], MSE of 1.440 (95% CI: 1.211-1.697), MAE of 0.895 (95% CI: 0.818-0.981) and R2 of 0.902 (95% CI: 0.882-0.918). In the SHAP analysis, the top 5 modifiable features at the population level were: level of health literacy, recognize self-weight status correctly, sedentariness duration on weekends, participation in professional sports training, frequency of staying up late.
CONCLUSION: This study developed a BMI prediction model for adolescents using readily accessible daily information. The model accurately predicts BMI values 1 year later and provides both population-level and individual-level interpretability. Compared to existing studies, it offers key advantages, including independence from complex clinical data, the ability to predict continuous BMI values, and strong model interpretability. Our findings provide a promising research tool for screening high-risk adolescents, informing public health prevention and intervention strategies, and supporting personalized clinical interventions.